{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unlocking the Black Box: How to Visualize Data Science Project Pipeline with Yellowbrick Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No matter whether you are a novice data scientist or a well-seasoned and established professional working in the field for a long time, you most likely faced a challenge of interpreting results generated at any stage of the data science pipeline, be it data ingestion or wrangling, feature selection or model evaluation. This issue becomes even more prominent when the need arises to present interim findings to a group of stakeholders, clients, etc. How do you deal in that case with the long arrays of numbers, scientific notations and formulas which tell a story of your data set? That's when visualization library like Yellowbrick becomes an essential tool in the arsenal of any data scientist and helps to undertake that endevour by providing interpretable and comprehensive visualization means for any stage of a project pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this post we will explain how to integrate visualization step into each stage of your project without a need to create customized and time-consuming charts, while getting the benefit of drawing necessary insights into the data you are working with. Because, let's agree on that, unlike computers, human eye perceives graphical represenation of information way better, than it does with bits and digits. Yellowbrick machine learning visualization library serves just that purpose - to \"create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models and assist in diagnosing problems throughout the machine learning workflow\" ( http://www.scikit-yb.org/en/latest/about.html ).\n",
    "\n",
    "For the purpose of this exercise we will be using a dataset from UCI Machine Learning Repository on Absenteeism at Work ( https://archive.ics.uci.edu/ml/machine-learning-databases/00445/ ). This data set contains a mix of continuous, binary and hierarchical features, along with continuous target representing a number of work hours an employee has been absent for from work. Such a variety in data makes for an interesting wrangling, feature selection and model evaluation task, results of which we will make sure to visualize along the way.\n",
    "\n",
    "To begin, we will need to pip install and import Yellowbrick Pyhton library. To do that, simply run the following command from your command line: \n",
    "$ pip install yellowbrick\n",
    "\n",
    "Once that's done, let's import Yellowbrick along with other essential packages, libraries and user-preference set up into the Jupyter Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "from cycler import cycler\n",
    "import matplotlib.style\n",
    "import matplotlib as mpl\n",
    "mpl.style.use('seaborn-white')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC, NuSVC, SVC\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import Ridge, Lasso, ElasticNet\n",
    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression, SGDClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier, RandomTreesEmbedding, GradientBoostingClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from yellowbrick.features import Rank1D\n",
    "from yellowbrick.features import Rank2D\n",
    "from yellowbrick.classifier import ClassBalance\n",
    "from yellowbrick.model_selection import LearningCurve\n",
    "from yellowbrick.model_selection import ValidationCurve\n",
    "from yellowbrick.classifier import ClassPredictionError\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "from yellowbrick.features.importances import FeatureImportances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Ingestion and Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to proceed with downloading a zipped archive containing the dataset directly from the UCI Machine Learning Repository and extracting the data file. To perform this step, we will be using the urllib.request module which helps with opening URLs (mostly HTTP) in a complex world."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning file download...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('/Users/Yara/Downloads/Absenteeism_at_work.zip',\n",
       " <http.client.HTTPMessage at 0x20445c48550>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import urllib.request\n",
    "\n",
    "print('Beginning file download...')\n",
    "\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00445/Absenteeism_at_work_AAA.zip'  \n",
    "\n",
    "urllib.request.urlretrieve( url ## , Specify a path to folder you want the archive to be stored in, e.g. '/Users/Yara/Downloads/Absenteeism_at_work_AAA.zip')   \n",
    "                          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unzip the archive and extract a CSV data file which we will be using. Zipfile module does that flawlessly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "         \n",
    "fantasy_zip = zipfile.ZipFile('C:\\\\Users\\\\Yara\\\\Downloads\\\\Absenteeism_at_work_AAA.zip')\n",
    "fantasy_zip.extract('Absenteeism_at_work.csv', 'C:\\\\Users\\\\Yara\\\\Downloads')\n",
    " \n",
    "fantasy_zip.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data and place it in the same folder as your Python code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('C:\\\\Users\\\\Yara\\\\Downloads\\\\Absenteeism_at_work.csv', 'Absenteeism_at_work.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at a couple of randomly selected rows from the loaded data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Reason for absence</th>\n",
       "      <th>Month of absence</th>\n",
       "      <th>Day of the week</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Transportation expense</th>\n",
       "      <th>Distance from Residence to Work</th>\n",
       "      <th>Service time</th>\n",
       "      <th>Age</th>\n",
       "      <th>Work load Average/day</th>\n",
       "      <th>...</th>\n",
       "      <th>Disciplinary failure</th>\n",
       "      <th>Education</th>\n",
       "      <th>Son</th>\n",
       "      <th>Social drinker</th>\n",
       "      <th>Social smoker</th>\n",
       "      <th>Pet</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Absenteeism time in hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>225</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>378.884</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>169</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>361</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>268.519</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>172</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>361</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>264.604</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>172</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>225</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>284.853</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>169</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>361</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>222.196</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>172</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>291</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>294.217</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>169</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>289</td>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>308.593</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>172</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>235</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>264.249</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>178</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>30</td>\n",
       "      <td>230.290</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>171</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>248</td>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>47</td>\n",
       "      <td>265.017</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>165</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  Reason for absence  Month of absence  Day of the week  Seasons  \\\n",
       "208  28                  19                 5                3        3   \n",
       "540  10                  22                11                3        4   \n",
       "732  10                  22                 7                4        1   \n",
       "524  28                  13                10                5        4   \n",
       "629  10                  19                 3                2        2   \n",
       "277  19                   0                 9                3        1   \n",
       "136  11                  22                 1                5        2   \n",
       "609  25                  25                 2                2        2   \n",
       "460  22                  23                 7                5        1   \n",
       "294  33                   0                10                6        4   \n",
       "\n",
       "     Transportation expense  Distance from Residence to Work  Service time  \\\n",
       "208                     225                               26             9   \n",
       "540                     361                               52             3   \n",
       "732                     361                               52             3   \n",
       "524                     225                               26             9   \n",
       "629                     361                               52             3   \n",
       "277                     291                               50            12   \n",
       "136                     289                               36            13   \n",
       "609                     235                               16             8   \n",
       "460                     179                               26             9   \n",
       "294                     248                               25            14   \n",
       "\n",
       "     Age  Work load Average/day             ...              \\\n",
       "208   28                 378.884            ...               \n",
       "540   28                 268.519            ...               \n",
       "732   28                 264.604            ...               \n",
       "524   28                 284.853            ...               \n",
       "629   28                 222.196            ...               \n",
       "277   32                 294.217            ...               \n",
       "136   33                 308.593            ...               \n",
       "609   32                 264.249            ...               \n",
       "460   30                 230.290            ...               \n",
       "294   47                 265.017            ...               \n",
       "\n",
       "     Disciplinary failure  Education  Son  Social drinker  Social smoker  Pet  \\\n",
       "208                     0          1    1               0              0    2   \n",
       "540                     0          1    1               1              0    4   \n",
       "732                     0          1    1               1              0    4   \n",
       "524                     0          1    1               0              0    2   \n",
       "629                     0          1    1               1              0    4   \n",
       "277                     1          1    0               1              0    0   \n",
       "136                     0          1    2               1              0    1   \n",
       "609                     0          3    0               0              0    0   \n",
       "460                     0          3    0               0              0    0   \n",
       "294                     1          1    2               0              0    1   \n",
       "\n",
       "     Weight  Height  Body mass index  Absenteeism time in hours  \n",
       "208      69     169               24                          8  \n",
       "540      80     172               27                          8  \n",
       "732      80     172               27                          8  \n",
       "524      69     169               24                          1  \n",
       "629      80     172               27                          8  \n",
       "277      65     169               23                          0  \n",
       "136      90     172               30                          3  \n",
       "609      75     178               25                          3  \n",
       "460      56     171               19                          2  \n",
       "294      86     165               32                          0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "740"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.ID.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, selected dataset contains 740 instances, each instance representing an employed individual. Features provided in the dataset are those considered to be related to the number of hours an employee was absent from work (target). For the purpose of this exercise, we will subjectively group all instances into 3 categories, thus, converting continuous target into categorical. To identify appropriate bins for the target, let's look at the min, max and mean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.924324324324324\n",
      "0\n",
      "120\n"
     ]
    }
   ],
   "source": [
    "# Getting basic statistical information for the target\n",
    "print(dataset.loc[:, 'Absenteeism time in hours'].mean())\n",
    "print(dataset.loc[:, 'Absenteeism time in hours'].min())\n",
    "print(dataset.loc[:, 'Absenteeism time in hours'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If approximately 7 hours of absence is an average value accross our dataset, it makes sense to group records in the following manner:\n",
    "\n",
    "1) Low rate of absence (Low), if 'Absenteeism time in hours' value is < 6;\n",
    "\n",
    "2) Medium rate of absence (Medium), if 'Absenteeism time in hours' value is between 6 and 30;\n",
    "\n",
    "3) High rate of absence (High), if 'Absenteeism time in hours' value is > 30.\n",
    "\n",
    "Upon grouping, we will be further exploring data and selecting relevant features from the dataset in order to predict an absentee category for the instances in the test portion of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Absenteeism time in hours'] = np.where(dataset['Absenteeism time in hours'] < 6, 1, dataset['Absenteeism time in hours'])\n",
    "dataset['Absenteeism time in hours'] = np.where(dataset['Absenteeism time in hours'].between(6, 30), 2, dataset['Absenteeism time in hours'])\n",
    "dataset['Absenteeism time in hours'] = np.where(dataset['Absenteeism time in hours'] > 30, 3, dataset['Absenteeism time in hours'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Reason for absence</th>\n",
       "      <th>Month of absence</th>\n",
       "      <th>Day of the week</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Transportation expense</th>\n",
       "      <th>Distance from Residence to Work</th>\n",
       "      <th>Service time</th>\n",
       "      <th>Age</th>\n",
       "      <th>Work load Average/day</th>\n",
       "      <th>...</th>\n",
       "      <th>Disciplinary failure</th>\n",
       "      <th>Education</th>\n",
       "      <th>Son</th>\n",
       "      <th>Social drinker</th>\n",
       "      <th>Social smoker</th>\n",
       "      <th>Pet</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Absenteeism time in hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>239.554</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>172</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "      <td>239.554</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>178</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>51</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>239.554</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>279</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>39</td>\n",
       "      <td>239.554</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>289</td>\n",
       "      <td>36</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>239.554</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>172</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Reason for absence  Month of absence  Day of the week  Seasons  \\\n",
       "0  11                  26                 7                3        1   \n",
       "1  36                   0                 7                3        1   \n",
       "2   3                  23                 7                4        1   \n",
       "3   7                   7                 7                5        1   \n",
       "4  11                  23                 7                5        1   \n",
       "\n",
       "   Transportation expense  Distance from Residence to Work  Service time  Age  \\\n",
       "0                     289                               36            13   33   \n",
       "1                     118                               13            18   50   \n",
       "2                     179                               51            18   38   \n",
       "3                     279                                5            14   39   \n",
       "4                     289                               36            13   33   \n",
       "\n",
       "   Work load Average/day             ...              Disciplinary failure  \\\n",
       "0                 239.554            ...                                 0   \n",
       "1                 239.554            ...                                 1   \n",
       "2                 239.554            ...                                 0   \n",
       "3                 239.554            ...                                 0   \n",
       "4                 239.554            ...                                 0   \n",
       "\n",
       "   Education  Son  Social drinker  Social smoker  Pet  Weight  Height  \\\n",
       "0          1    2               1              0    1      90     172   \n",
       "1          1    1               1              0    0      98     178   \n",
       "2          1    0               1              0    0      89     170   \n",
       "3          1    2               1              1    0      68     168   \n",
       "4          1    2               1              0    1      90     172   \n",
       "\n",
       "   Body mass index  Absenteeism time in hours  \n",
       "0               30                          1  \n",
       "1               31                          1  \n",
       "2               31                          1  \n",
       "3               24                          1  \n",
       "4               30                          1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's look at the data now!\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the target is taken care of, time to look at the features. Those of them storing unique identifiers and / or data which might 'leak' information to the model, should be dropped from the data set. For instance, 'Reason for absence' feature stores the information 'from the future' since it will not be available in the real world business scenario when running the model on a new set of data. Therefore, it is highly correlated with the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(['ID', 'Reason for absence'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month of absence', 'Day of the week', 'Seasons',\n",
       "       'Transportation expense', 'Distance from Residence to Work',\n",
       "       'Service time', 'Age', 'Work load Average/day ', 'Hit target',\n",
       "       'Disciplinary failure', 'Education', 'Son', 'Social drinker',\n",
       "       'Social smoker', 'Pet', 'Weight', 'Height', 'Body mass index',\n",
       "       'Absenteeism time in hours'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now left with the set of features and a target to use in a machine learning model of our choice. So, let's separate features from the target, and split our dataset into a matrix of features (X) and an array of target values (y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Month of absence', 'Day of the week', 'Seasons',\n",
    "       'Transportation expense', 'Distance from Residence to Work',\n",
    "       'Service time', 'Age', 'Work load Average/day ', 'Hit target',\n",
    "       'Disciplinary failure', 'Education', 'Son', 'Social drinker',\n",
    "       'Social smoker', 'Pet', 'Weight', 'Height', 'Body mass index']\n",
    "target = ['Absenteeism time in hours']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop(['Absenteeism time in hours'], axis=1)\n",
    "y = dataset.loc[:, 'Absenteeism time in hours']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up some visual preferences prior to visualizing data\n",
    "class color:\n",
    "   PURPLE = '\\033[95m'\n",
    "   CYAN = '\\033[96m'\n",
    "   DARKCYAN = '\\033[36m'\n",
    "   BLUE = '\\033[94m'\n",
    "   GREEN = '\\033[92m'\n",
    "   YELLOW = '\\033[93m'\n",
    "   RED = '\\033[91m'\n",
    "   BOLD = '\\033[1m'\n",
    "   UNDERLINE = '\\033[4m'\n",
    "   END = '\\033[0m'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Analysis and Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whenever one deals with a categorical target, it is important to remember to test the data set for class imbalance issue. Machine learning models struggle with performing well on imbalanced data where one class is overrepresented, while the other one is underrepresented. While such data sets are representative of the real life, e.g. no company will have majority or even half of its employees missing work on a massive scale, they need to be adjusted for the machine learning purposes, to improve algorithms' ability to pick up patterns present in that data.\n",
    "\n",
    "And to check for the potential class imbalance in our data, we will use Class Balance Visualizer from Yellowbrick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Low: \u001b[0m 468\n",
      "\u001b[1m Medium: \u001b[0m 244\n",
      "\u001b[1m High: \u001b[0m 28\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating population breakdown by target category\n",
    "Target = y.value_counts()\n",
    "print(color.BOLD, 'Low:', color.END, Target[1])\n",
    "print(color.BOLD, 'Medium:', color.END, Target[2])\n",
    "print(color.BOLD, 'High:', color.END, Target[3])\n",
    "\n",
    "# Creating class labels\n",
    "classes = [\"Low\", \"Medium\", \"High\"]\n",
    "\n",
    "# Instantiate the classification model and visualizer\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['red', 'limegreen', 'yellow'])\n",
    "forest = RandomForestClassifier()\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "visualizer = ClassBalance(forest, classes=classes, ax=ax)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='x')\n",
    "\n",
    "visualizer.fit(X, y)  # Fit the training data to the visualizer\n",
    "visualizer.score(X, y)  # Evaluate the model on the test data\n",
    "g = visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an obvious class imbalance here, therefore, we can expect the model to have difficulties learning the pattern for Medium and High categories, unless data resampling is performed or class weight parameter applied within selected model if chosen algorithm allows it.\n",
    "\n",
    "With that being said, let's proceed with assessing feature importance and selecting those which will be used further in a model of our choice. Yellowbrick library provides a number of convenient vizualizers to perform feature analysis, and we will use a couple of them for demonstration purposes, as well as to make sure that consistent results are returned when different methods are applied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rank 1D visualizer utilizes Shapiro-Wilk algorithm that takes into account only a single feature at a time and assesses the normality of the distribution of instances with respect to the feature. Let's see how it works!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating 1D visualizer with the Sharpiro feature ranking algorithm\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "visualizer = Rank1D(features=features, ax=ax, algorithm='shapiro')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "visualizer.fit(X, y)\n",
    "visualizer.transform(X)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rank 2D Visualizer, in its turn, utilizes a ranking algorithm that takes into account pairs of features at a time. It provides an option for a user to select ranking algorithm of their choice. We are going to experiment with covariance and Pearson, and compare the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate visualizer using covariance ranking algorithm\n",
    "figsize=(10, 7)\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "visualizer = Rank2D(features=features, ax=ax, algorithm='covariance', colormap='summer')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "visualizer.fit(X, y)\n",
    "visualizer.transform(X)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate visualizer using Pearson ranking algorithm\n",
    "figsize=(10, 7)\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "visualizer = Rank2D(features=features, algorithm='pearson', colormap='winter')\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "visualizer.fit(X, y)\n",
    "visualizer.transform(X)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visual representation of feature correlation makes it much easier to spot pairs of features, which have high or low correlation coefficients. For instance, lighter colours on both plots indicate strong correlation between such pairs of features as 'Body mass index' and 'Weight'; 'Seasons' and 'Month of absence', etc.\n",
    "\n",
    "Another way of estimating feature importance relative to the model is to rank them by feature_importances_ attribute when data is fitted to the model. The Yellowbrick Feature Importances visualizer utilizes this attribute to rank and plot features' relative importances. Let's look at how this approach works with Ridge, Lasso and ElasticNet models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5FsBhwJKSjrR9TF9eQ0RERER3ZbpFtBkm6Ya2H+BLwCzAIrZXB9YGDpM0Z8MxhwG/qxUBL2XiD11n1bWSnwTWBY4FHk6AHBEREVODZJKjzSjb27W9kHQcMBuwQg2aAWYAFmo4ZglKMREo6zI3uqc+jmXiktcRERERLS+Z5OjMm8D1NSM8DLgEeLxh/0PUqn3AKk3HNi/APY78vkVERMRUIkFLdOY14PVa8e8eYLzt1xr2HwdsKul6SnXAdzrp63lgRknHT7HRRkRERPSSVNyLHpO0EfCC7b9IWgc41PawnvaXinsRERHRRyZZcS9zkmNyPAGcLeldYDDwtX4eT0RERESvSJAcPWb7ESbMSY6IiIgYMBIkR0RERAwgKUvdO/LFvR6oZZzHS9q2afsDks7pQX/LSFqrPv9ASecu9rG3pNHNY+qg7Q2SFu/uOSIiIiKmFQmSe24MsH3bC0nLAB/uYV9bAUtO5ni2BL5s++LJ7CciIiJimpfpFj13P7CYpDltvwzsCFwAfBJA0g7AAcBbwN+APYAdgI0oxTU+DRwPXAvsDLwt6d7a90QlnW2/1HZSSQsDZ1EKe4ynfFluZeDzwFmStrX9RG07O3AmMCcwL3CG7VNrV8dImreOb6e67WLKB6cZgOG2H5S0H6X63nhghO2Tarb8LWBhYH5gZ9v3StoV2IvyJb4rbH9H0jbAgcB7wC22D+nBvY6IiIjoU8kkT57LgC0kDQJWAm4DkDQPcDQwzPYawMvAnvWYOWxvAmwKHGL7GeAc4ATbd9U2zSWdG/0YOMn2WsD+te3pwGhgp7YAuVqUEtiuB2xCCVbfH3tdru1K4Nt1/K8AG1IC79klLQlsC6xRfzaXpHr8P2yvD5wM7CHpo8AhwJrACsAckj5Z78MX6n34uKTm64mIiIhoOckkT54LgVMpVegayzJ/CvhrQ+GNm4D1gDspwSzA00BHc487K+m8RO0P26MlLdjJ+MYCB0jaEniVkiFuc1N9vA3YGDgI+AxwBaUoyPeApSllqK+rbeeiBN4A9zVcx+r1mh+y/Ubd/nVJKwEfAa6usfVstV1ERERES0smeTLYfpwyD/lrwPkNu54AlpTUNkd5CPBofd5e9Zbmks2dVXh5hJKtRdJylEC4IwcBt9veEbiUiRfOXqk+rkkpLz0UeK5mnb8HfB8w8Fdg7ZrZPgd4sIMxPgYsLmmmOrbfAP+iBNHr1uNPpnxQiIiIiGhpCZIn38XAgrbbgmBsvwgcBVwv6Q7KfOBTOzgeSuZ4X0lrd+F8BwH7Sbqp9rlrJ22vBPaXdAtlfvS7bUEsZerEDZTpHMdR5ljvLul24EfAD2zfT8ki3yLpbkqm+Zn2TmT7Bcoc6xtrH/fa/gdwQt12J2Uqx6PtHR8RERHRSlKWOlpGylJHREREH5lkWepkkiMiIiIimuSLexEREREDSGcV91Jhr+uSSR5gajXAEU3bjpO0s6TlJB1Zt20haYGmdjNL2m0KjWuK9R0RERHR2xIkT0Nsj7Z9TH25PzB7U5P5gCkVyE7JviMiIiJ6VaZbTEMkDQWGA78GlgPOk7SG7bdrk8MoS9cdCZxNWT1jZmAe4Bjbv5P0EGWFireA/ShrRc9EWS5umO1FJQ0BjqVU2XuMUkjl/b4bAvWIiIiIlpRM8sA0TNINbT+UstLvs30VEyr0vd2w61jg4RrELg78xPa6wL7APrXNrMB3bW9PCXx/Z3sIZR3m6Wv1wTOALev2Zyhltxv7joiIiGhpySQPTKNsb9f2QtJxPejjOeBwSbtSCoc0VutzfVwCOLc+b6s4+BFgfuCSWmVvFuBPPTh/RERERL9JJnna1Vzlr3nbd4HzbH8ZuJ6J1xMcVx8fAlatz1epjy8C/wQ2q1X2jq3Ht3e+iIiIiJaUoGXadRtlTvLcDdueB2aUdDxl+sRJkm6mVOWbt50+jgM2lXQ9sDvwju1xlC8FXiXpNmBvSjDd2HdERERES0vFvegxSRsBL9j+i6R1gENtD+tpf6m4FxEREX1kkhX3Mic5JscTwNmS3gUGA1/r5/FERERE9IoEydFjth9hwpzkiIiIiAEjQXJERETEANJeWeqUo+6+fHEvekTSwZKekzRzf48lIiIiorclSI6e2gEYAWw3qYYRERERU5tMt4huq+WtHwN+CZwPnCNpJeAXwGuU5d7etL2zpP0oFf/GAyNsn9Q/o46IiIjoumSSoyd2A860beAtSStTAuad6xJwjwFIWhLYFlij/myuWoYvIiIiopUlSI5ukTQXsBGwv6SRwBzAvsACtv9am7WVqF4aWAi4DhgFzAMs2rcjjoiIiOi+BMnRXTsCZ9lez/YGwMrAesAbNXMME0pUG/grsHYtUX0O8GDfDjciIiKi+xIkR3ftBvy67YXt/wG/pQTAZ0v6M7ASpUT1/ZQs8i2S7gY+AzzT5yOOiIiI6KaUpY5eIWkf4BLbL0j6HvC27WO600fKUkdEREQfSVnq6DP/Av4k6XXgFeAr/TyeiIiIiB5LkBy9wvZvgN/09zgiIiKmdY0V91Jpr+cyJzkiIiIiosmAyiRL+gmwAjAf8CHgceAF29v068AaSNoCuNP2sx3snxvYwPaFkg4BRtm+q08HGRERETGNG1BBsu1vAEjaGVjc9iH9O6J27Q8MB9oNkoFlgU2BC20f12ejioiIiIj3DagguSO1jPLxwNvA6cAbwD5M+Gbj1pTCFwfXNosAF9s+VtKWdfs7wJPATsCRwOLAR4G5gP1s3yJpB+AA4C3gb8AewA7ALpSpLT8AlgPOk7QGcDTweWA24BHbXwUOAz4raQ9gNWAEZRm1s4FPA4OBE2xfLOkGYHQd++zANrb/0XDdM1Aq4X2mnv9w4F7gDkolvPdq/2vUbTcDSwH/Abav92Ki423fIOkB4EZKQD8e2AyYEbi4tpsBGG77wZSljoiIiKnRtDQneWbba9r+NbAYsHEtcGFg/dpmIWArYFXgW3Xb9sBPba8B/IkSjAL8r5Zg3hH4haR5KEHvsNr2ZWDP2vYl22vYvooS1O4EzFy3r0sJhleR9HHgWMoUi9Mbxr4n8KLt1YB1gO9Jmrfuu8v2OsC1dayNdqvHrUUJZH9h+1VgZ+AM4FfATnXbh4AL6tjH1HN+4Pja7+zARbaHUNY93pCyNvIr9fnXgNlTljoiIiKmVtNSkOyG588D50r6FSUbOkPd/qDtd23/l5JtBjgQWEvSjZRgdlzdPgqglmKeD/gU8Ffbr9X9N1Gyss3nbvMG8FFJFwGnAbM2jKPZErU/av8PU7LKAPfVx6cpgXejZYCNasb5t8D0kuapc5xfBv5le3Rt+47tm+rz2wB1dHwH572Gkl2+AjiGcp9SljoiIiKmStNSkDwOQNIclIzvdpRM6RtMmHbRXmWVPYDv1KzpIGCLun2F2t/SlGzqE8CSkj5c9w8BHm08d8Pz6SgZ1wVtbw8cCsxS+2/b3+gRYM16vtkowesTnYy5zRhKxndoPd+lwEuStgZeB96tzwFmkPTZ+nx1Sjnpdo/v4LxDgedsrwd8D/g+KUsdERERU6lpKUhu8ypwK2Vu7s2UIHmBTtrfBVwraRQlY/yHun15SdcBZwK7234ROAq4XtIdwLzAqe30dxtwHnA38Kna9jeUlTgWAB4DlpF0QMMxpwPzSLoFuAE42vbzXbjW04DFaxb8NuAfwILAdylfHhwOHCtpodr+4HqOj9djP3C87XG0735gd0m3Az8CfpCy1BERETG1SlnqHpD0HWCs7V/291h6i6QnKSuCvNlfY0hZ6oiIiOgjkyxLPS1mkiMiIiIiOpVMcrSMZJIjIiImX1tZ6pSk7tS0k0mWNErSSvX5jJJekXRQw/4bG76YNqm+npTUvFJE276F6zziyRnrBpLO6WDftpL+K6mzedJ9StIlkj7UtG1sf40nIiIiYkobMEEyZQ3jNevzNYE/AhsD1IB3wfpFsla3G3AyZVWNftf2YcH2//p7LBERERF9ZSBV3LsWOAL4CbARZdWJ4+uSb5+jrOGLpHUpS5S9CfybUg1vOSauyEdtOxxYD9je9lvNJ+ygr9coq0IsSFkX+BrbR0haglI177/156V2+lsEmJtSme9eScfWXY8An7X9X0nfBN6lrIhxOmWN4jcpQfVg4Mo6lquBOykrbkApFrKT7UclHUFZyu6Fuv0IyrrHZ9UxA3zN9oPAFygrdgyu51uKsgLHTHXMSwMnUD5wzUkpJDIrZcWPbWqbW4GtbT/XfM0RERERrWggZZLvoyxXNghYixIU/5lSoW4oMLLuOx3Ysq57fCOlVDNMXJEPYD9KRnqbDgLkjvpaELjD9vqUKnN71UO+CxxZq+Pd1sE17AqcbfsV4Pba9zuUQh5b1TbbUZaQ+zFwku216/Pj6v75gPVs/5AS0O5YKwP+HtimTjnZEFgR2ByYvx53KHBd7W8PJixftzFwVT1mZturAN+mBNfUc3yjXtcJwFcpH1iWkTRXrbr3YgLkiIiImJoMmCC5rt97P7ABZXm2tyhV4FanBKvXUtYuftV221q9nVXFWweY0/Z7HZyyo77+A6wo6QLgp9SMa913V31+a3NnNVO7I7C1pJGU0tn71t1nAjvVOdeP2v43paDIobUa3pHAR2vbJ2y/XZ8/A5xU5z+vTanotwSllPV7tt+grNdM7W+X2t8ZwFx1+ydsP9U4/vr66YZzHCHpXGBrYAbb44HzKWWyd6FkqCMiIiKmGgMmSK6upWREr6mvb6FMtcD2f4AXgdkltWVPO6qKB7AZpTrd8A7O1VFfOwMv296BMvXjQzXrPAZYtbZdsZ3+NgL+Yntt2xvYXgn4mKRlbf+N8i3Mb1ICWGp/B9dKdntSpl80X8eZwFdt7ww8W/v4KyWIn07STMDyDf39tPb3f8AFNev8YMP+VQHqlwo/XrefBBxl+yu1bdu3RX8FbEPJ6l/dwT2MiIiIaEkDMUhegxqU1Yzqy5QsLzXDuTtwWZ0nuw5lGkRHvgYcJOkzzTs66es6YCNJt1GmLPyNUklvb0rm9zpg5XbOtTvw66ZtZzIhm3wWJeC/vr4+CDiqVsM7D3ignT5/DdxZxzcbsECdZ3w1cAdwOfBO/TkW+L+aSR4JPESZavGHer1XAE9LuhM4kfIhAUrG+ApJN1Oy3wvU9s9Q5mdfZ/vddsYWERER0bKyTvI0RtJHKV+iO6Vmkv8KDKtTKHr7XH8ADrD99660zzrJERER0UcmuU7yQFrdIrrmRcp0i78A44EzeztAljQLZarLyK4GyBERERGtJJnkaBnJJEdEREy+owcdnWp7kzbtVNyLiIiIiOgtCZKnUT0t411LandYDVDSd9pbEUTSFq1UajsiIiKiMwmSp109KuNte6Tt05u3d8H+wOw9HGtEREREn8oX96ZdkyzjLWkIZWm49yilqPcEdgAWt31IB+WtATaTtA2lxPURlLWblwPOk7RGQ7GTiIiIiJaUTPK0a5JlvCmFS9rKbj9DKZQCQCflrQGesf0F4ABgL9tXAaOBnRIgR0RExNQgQfI0qgtlvK+nBL6X1AIj6wGfbOiio/LWAPfUx7GUDHNERETEVCVB8rStwzLelPWU/wlsVktVH8uEan/QcXlrKOsvNxtHft8iIiJiKpGgZdrWYRnvmmneH7iqltjem1Kqmtq2o/LWHbmNMid57ilwHRERERG9KsVEokemRHnrFBOJiIiIPpKy1DHFTPHy1hERERH9JUFy9EidjvHV/h5HRERExJSQILkfSTqEsuTaOEo29lDb93R+1ETHzwccaXvvDvYPBYbb3q4Xhtte/wsDI2yvMiX6j4iIiOgvCZL7iaQlgU2B1W2Pl7QccC7wgVLQHbE9lvKFuoiIiIjoRQmS+8/zlHWHd5E00vZoSSsBSFoeOJlS6e5NYHfbT0k6nFK4Y3rgVEop6RG2V5G0NbAPEyaib93eSSV9BLiYsrLJDMBw4LW67WlgYWAEsDRlWberbB/a3pga+hwMnAM8ZPt4SfsBX6Jkx0fYPknSOZQKfPMAG9t+aTLuXURERMQUlSXg+ontF6mZZOB2SWOATeruM4B9a6W7U4ATapC6IbAysBqwJBN/M3MxSvA5FDCwfgenXgl4pfb1NWD2uv1TwK5GkDhHAAAgAElEQVR1DN8FDqzn2rWjMdXt0wMXALfXAHlJYFvK0nJrAJtLUm07yvZqCZAjIiKi1SWT3E8kLQq8anuX+vrzwNWSrgcWsD26Nr0JOA4QtcId8D9g/zonuM3zwLmSXgcWB27v4NTXAJ8BrqCsa/y9uv1x269Iegv4l+3/1HG1rRHY3pigTA95FZi1vl4aWAi4rr6eC1i0Pvckb0xEREREC0gmuf8sC5wqaeb6+lFKhvc94FlJy9btQ+q+McDnaoW7GSRdC8wEIGkO4GhgO2A34A06Xv9vKPCc7fUoAfL36/ZJLZjd3piglKDeGPhy3W/Kmslr16z2OcCDte24SZwjIiIioiUkk9xPbF8maQngzpr9nQ74Zs3m7g78XNIg4F1gV9uPSxoJ3Frbngq8Vbt7tW6/F/gv8BKwAPBEO6e+H7hY0gGUgPyYLg75A2NquJY3JA0HzqNM0bgOuKUWGbkLeKaL54iIiIhoCam4Fy0jFfciIiKij0yy4l6mW0RERERENEmQHBERERHRJEFyRERERESTfHFvAKjlpy8BHm7Y/ILtbRraDAfms/2dXjjfWsDLth+QdJntLSe3z4iIiIhWkiB54Bhle7s+OtculKp8DyRAjoiIiIEoQfIAJmkN4GfAfyjLvd1RC5CMsL1KbXMHZX3l/1HWNJ6T8o3PnSjrLZ8KzEwpJ30MpXT1BpQ1mx+mFDiZr4Oy1dMBF9VjPl3b7jXFLzwiIiJiMmVO8sAxTNINDT/fBH4KbG97XdpfM7nRYcDvba9Wn69Eqdz3k3r8vsA+tu8BRgLfsv1Uw/Edla1ejLKm8krARpLm65WrjYiIiJiCkkkeOD4w3ULS1223Vca7lQnloRu1rRMo4GwA26Pq8UsBh0valVKRb4ZOzt9R2eq/236t9vccJSsdERER0dKSSR7YxtaqfgAr1sc3gY9KGixpTmCRuv2RtjaS1pJ0PPBd4DzbXwauZ0JAPY4P/u50VLY61WoiIiJiqpNM8sAxTNINTdu+DJwr6TXgNeAl22MlXQv8Bfh7/QH4PnC2pB0pge2ulBLTJ0kaS5lXPG9teydwnKTGKRwdlq2OiIiImNqkLHW0jJSljoiIiD6SstQREREREd2VIDkiIiIioknmJLcwSYcA61C+KDceOLQuwdbT/kYAO9l+uwfHzgzsaPtMSTsD/7H9+56OJSIiIqKVJUhuUZKWBDYFVrc9XtJywLnAZ3va52RW5JsP2A040/Y5k9FPRERERMtLkNy6ngc+CewiaaTt0ZJWApC0DHASZdL5vyllopcHjgfeBv4MDLE9rLb/A3AEcDmlQMiCwJnAjJRKe9tR1i8+vT6+Cexh++mG8RwGLCnpSMo0nbHAGODbwFu1z18CwyiB/M9snyppCHAspRLfY8Cett/p3VsVERER0bsyJ7lF2X6RmkkGbpc0Btik7j6DUv1uKHA18K26fWbba9o+GphF0kKS5gfmtX1fQ/c/Bn5ge1XgNEqA/WPgJNtr1+fHMbFjgYdtH9O0/RPAVsBewOGUZec2BPasy8GdAWxZK/E9A+zc03sSERER0VeSSW5RkhYFXrW9S339eeBqSdcDSwCnSIJSBa+tcIcbujgL2ImS5f1Vc/fA7QC2L6n9nwgcKulgSoa6q/OWH7L9jqSXgcdsvy3pJUpG+iPA/MAldayzAH/qYr8RERER/SZBcutaFthL0hdtv0kJhF+hTFsw5Qt4T0lanRKIQvmCX5sRwHWUL/yt19R3W3W9P0vaAZibMnXix7Zvk7Q4pWpeo/aq7EHnFfVeBP4JbGb7FUmbAq93dtERERERrSBBcouyfVktKX2npNcpAeo3a7C5F3CepMG1+a7AAk3Hvy7pfmB62682df9N4DRJh1PmJO8IXAWcWlexmAXYv+mY54EZa7nqN7p4DeMk7Q9cJWk64FVKdjsiIiKipaXiXrSMVNyLiIiIPpKKexERERER3ZUgOSIiIiKiSYLkiIiIiIgm+eLeZJI0FLgEeJgyv2UGYBfbY7p4/B3AdrafnFJj7K66HNwJtp/qQtvjgDGpwhcREREDSYLk3jGqreSzpPUoxTg26fyQ1mX7gP4eQ0RERER/SpDc++YCngSQtDxwMmVt4zeB3evaxscCGwBPA/PWtrfV/X+VtCGwie192jqVdANwP7A0Za3hm4H1gTkp6yC/Ryk1PWft84xaFnpv4CuUdY5vsf1NSVsCBwPv1LHuZHtc07mGU8pVLwJ8FFgI+LrtP0railJd7wVKaesx9bgfAGtRpvGcQCmDfRNwNDAaGAVs0FTuOiIiIqLlZE5y7xgm6QZJtwNnA7+p288A9q0lmU8BTpC0NCWQXJGyZvBsDW2/Up/vQqmY1+wu218AZgL+Z3tdyjSPIcCiwAjb61Gy2AfWY74K7F9LUD8uaXpge+CnttegVMCbvZNre8v2hpR1k79et/0QWIcSpP8PoAb2i9heHVgbOAyYFfgS8BPgfOCgBMgRERExNUiQ3DtG2R5aA9HPAZdJmgVYwPbo2uYmYKn6c7ftcbXIx4N1/8XAppI+Cixo+952ztO27WVKcAzQVgJ6LLC5pPMpWd4Z6v6vAsMl3UjJBg+iBNBr1W2rMXGlvmb31cengZklfYxSLvvftscDt9X9ywAr1Cz0yHr+hepc61so2eiRnZwnIiIiomUkSO59/2p4/qykZevzIZTS0gZWkjSdpA8DSwLY/h9wPfAz4Ncd9N1Z5ZeDgNtt7whcyoRFsncHhtds9vKUoHgP4Dt12yBgi076bT7nv4E5JH2kvl6xPo4Brrc9FBhG+TLj45JWoUwRuQn4RifniYiIiGgZmZPcO4bVDOp7lOkTB9p+Q9LuwM8lDQLeBXa1/bikS4G/AM9Syj23OQO4FdirB2O4klJWegdKIPuupJkomeq/SHoBeAa4kzK94lpJ/wZeA/7Q1ZPYflfSV4E/SvoPZV5z2/mHSrqZMs3icsqHsLMoQfhTlBLbN9i+uwfXFxEREdFnUpa6hUhaEdjP9k79PZb+kLLUERER0UcmWZY6meQWIWlfyhf2turvsURERERM6xIktwjbPwd+3t/jiIiIiKnT0YOOBuCo8Uf180gGhgTJ0WNN1QbHA7MAF9g+uYP2+9YPAxEREREtLatbxORqW/5ubcoKHt+QNGcHbQ/vw3FFRERE9FgyydGbZqOs8LGgpMspk+L/TZlrvS8wt6RTbO/dj2OMiIiImKRkkmNytVUbHAVcAOxHWcpun7pm8tXAt2wfC/wnAXJERERMDZJJjsk1yvZ2jRskXQScIglK5b1H+2NgERERET2VIDmmBAM72X5K0urA/HX7JNckjIiIiGgFmW4RU8JewHm1+t5xwAN1+8OSzu+/YUVERER0TSruRctIxb2IiIjoI5P863YyyRERERERTTInOSIiImIq11ZtD1Jxr7ckkxwRERER0aTTTHJT2eFBlOW8TrR9iaTlgE1tH9PBsWsBL9t+oL39valWeLsGeM32er3c987AMcDjddOcwK229+lmPycCJ9h+qmHb4sAv63rCU5SkuYENbF/YhbZnAyNtX1JfPwJcZ3vf+vpc4DLbV3ShrxuA4bbHTM74IyIiIvpSV6ZbvL8OrqRZgRslPWp7NDC6k+N2AUYwYWWDKWlp4FnbW02h/i+0fQiApOmAmyV93vbdXe3A9gFTaGxdtSywKTDJIBn4E7AmcImkTwGPAUMb9q9GqaAXERERMSB1a06y7dclnQZsXbO3w21vJ+kc4NPAzMCPgb8DGwCfk/QwJTjbkpKJfqU+/xKwEfCheuzxts+RtDLwM0rm+hlgB2BR4CQayhzbfgVA0ozAz4EFJB0NLATMU382Bg4H1qiXcKHtn9XxvlPbzkQJ5r8IfBLYzPZjndyG2SjZ5FckzQD8EvgMZerK4bZvkHQsMKxuu8j2iW0Z1Xr9F9RrGdvWqaQhwLGUss6PAXvWa5/se1QdBnxW0h6UIPis+n6MB75m+/6Gtn8GvlWfbwz8HthU0pLAG8A/bb8maXng5DrmN4Hd6zVfWcdwdcP1fRE4ENjC9sud3N+IiIiIfteTOcn/AuZteyFpNmBtSuC7ITDY9j3ASEqg9U9KwLqO7TUpgdmK9fA5bG9CCaIPqdtOB75qe2VKsLYE7ZQ5bju/7beBAygZ77aZ6qNsrwasDiwCrEIJlL8kaZna5sk6NeMRYBHbGwG/pQTLzb4k6UZJjwKjgGNt/w3YDXjR9lrAZsAvavudKB8C1qIElY2+QQmc1wZ+V+/hoHqNW9oeQgl8d+6te1QdW+/L6ZQPMifVce9PCZjfZ/tFYJykOSjv6TX1Z0NKRnlkbXoGsG8d8ynACXX7fMB6tn9YX29JyTxvkgA5IiIipgY9CZIXogS+ANh+jRIAnQ5cTMnM0rB/HPA2cJGks4BPUAJlmDBd42lKFhrgY7YfqceeYvteShB4Ss3G7gIsMIkxuj4uAdxse7ztd4A7gCXrvnvr48uUOdcALzWMo9GFNRBcH5iVCWWWlwE2quP6LTC9pHmA7YAfAH+kZJ0bLQXcVZ/fWh8/QqlKd0ntaz1KVhumzD1aAripHj8aWLCdNtcB6wDz2n6aEiSvBgxhQpC8QD2e2t9S9fkT9cNLmy8Ac1Oy9xEREREtr1tBcs0a7w5c2rBtfmAF21tQ/jT/Q0nTA+OA6SQtC2xue1tgv3rOtgWc26tk8qykz9S+D5a0BRPKHA+lZEivmsRQx9XHR6hTLerUiNWAv3Vy7k7ZfgLYB7hU0oeAMZSs8FBKlvVS4HVgG2B7ypSLnSUt1NDNGGDV+rwto/4i5YPHZrWvY4HrOxlnT+7ROCa8349Q5hxTv4A5lg+6lpKhv6Fe++OUQHfRhqkZz9b3F0rw3PbhYRwT24fygaHdL3lGREREtJquBMnDJN0g6TrgD8BRtt2wfywwn6T7KIHVj22/C9xJKUn8HvBfSXfX/c/ReZZzT+BsSTcCy1OmDnRU5rhTtv8APCHpdkoW+Tc169pjtv9MmeJwNHAasHgd623AP2y/BfyHkgEeRZn/+1RDF0cAX6wZ301rn+Mo0x6uknQbsDfwUCfD6Mk9egxYRtIBwEHAfpJuAk4Fdm3nHLcAK9Awrxi4nwkfMqB8YPp5Pef+wNc7GfMxwAaS1uykTURERERLSFnqaBkpSx0RERF9JGWpIyIiIiK6K2WpIyIiIlpYY8nprkhZ6t6RIHkAk3QIZYWKcZQvAB5al+eLiIiIiE5kusUAVQt/bAqsW9eDPhg4u39HFRERETF1SCZ54HqestbyLpJG2h4taaVaTGWiynyUZetOo6yXPA9wje0jJG1JCa7fAZ6kFEmZHTi/Pk5PqTI4StIDwI2U8tfjKcVVZqSsnT0dZW3s4bYf7IuLj4iIiJgcySQPULVq3qaUqoO3SxoDbEL7lfkWBO6wvT5lXem9ajfbAz+1vQZlKbvZKWW+r63V+rYBzpI0Xd13UUPFwA2BlShluDcEvlbbRERERLS8ZJIHKEmLAq/a3qW+/jwlKJ6FUpkPSnb3Ucq6zitKWht4lQlVEw8Evi1pL0oBkt9RqvVdAGD7GUmvUioGAtxXH9uqA14CfAa4gpKN/t6Uut6IiIiI3pRM8sC1LHCqpLZS1o9Ssrp/54OV+XYGXra9A/AT4EOSBgF7AN+p2eFBwBZMXK3v48BclGkb8MHqgEOB5+qc6O8B3+/1q4yIiIiYApJJHqBsXyZpCeBOSa9TPhB9k5LlPU/S4Np0V0rgO6JWw/svpareAsBdwLWS/g28Rqm4eCWl2t/WlKz0HrbfrZnpZvcDF9cqf++RstQRERExlUjFvWgZqbgXERERfSQV9yIiIiIiuivTLSIiIqKldbfi3LQuFfd6RzLJERERERFNkkmeAiQNpSx/9jBlzssMwIm2L5kC5zoPWAzY2faYuu2TwGdtXynpBkoRjzG9fe4ujq9fzx8RERHREwmSp5xRtrcDkDQrcKOkR22P7uXzrG/7Y03bhgGLU1aiiIiIiIhuSpDcB2y/Luk0YGtJD9JUAho4irKO8Uq2/1OLd8xq+0dtfUhal7LW8JtMKCf9fWAuSVfY3qy2GwwcQlnr+LZ6+FGSPgZ8GNje9uOSfgCsRZlyc4LtSxvOdQAwve0f13G/aXt/SYcDjwMP0lTa2vYrk+jzi5TiJFvYfrl37mxERETElJE5yX3nX8C8tFMC2vY4ShW77WrbLwPntR1YC3ucDmxZC3vcCBxue2/gP20BMoDt94DjgAtt/75uvsr2MEpAvrWkDYFFbK8OrA0cJmnOhrFeBmxQny8GrFKfr09ZK/kDpa0n0eeWwL7AJgmQIyIiYmqQTHLfWQj4Jx2XgD6LUnjjJmCs7X81HDsvpcT0M/X1TXSvet099XEsMB+wDLBCnS8MZc70QsDLALafkvQhSStRCo0sJGlF4BXbr9YiJc2lrTvqE+ALwOyU0tQRERERLS+Z5D4gaTZgd+BSOigBbfspSpB6GCVgbvQiMLuk+evrIZTAtCPjmPi9ba4YMwa4vmaCh1G+ZPh4U5urgB8CfwL+CJwMXF73mQ+Wtu6sz31qH6m4FxEREVOFBMlTzjBJN0i6jjJF4SjbBq4DNqrzhU9lQgloKNMY1gRGNnZkezwlyL5M0q3AOsB3Ozn3g8BmkrbrYP+VwOuSbqZkmcfbfq2pzWXA6sAoSoD7eeCKum8vSmnrmylTOx7oQp/HABvU0tcRERERLS1lqVuIpP8DlrZ9ZH+PpT+kLHVERET0kUmWpc6c5BYh6fuULPJmk2obEREREVNWguQWYfvQ/h7DtCClTSMiYqBLWerekSC5GzqrpCdpOWBT213+cpqkEZQvwL3dzr6FgRG2V+msXW9pr3Jf0/4TgRMo6zOPtf3LKTWWiIiIiP6WILn7Oquk161qem399Fa7ydRe5b7GMRwAUJd9i4iIiBjQEiRPhqZKenMCw21vJ+kc4NPAzMCPbV8saRNKZT2A+4DhlCXSFgd+SclMLwjMCuxEqawHgKQnG9q9BSwMzE/J+t4raV9KwY4ZgFfq8y9Rsr7TUVbC2M32NrW/W4GtbT9XX59CrdxHKWRyJjAnZX3mM2yfWtc/Ht4wpqFt11tfj7U9X732eerPxpQl4tqtwhcRERHRqrIE3ORrq6QHvL8m8tqUQHVDYLCk6YGfAxvbXpFSVOQTTf08VqvifYeyPnFH/lGr9Z0M7CFpOkpAuo7tNSmB8oq17Uu216CsdbyMpLkkLQm82BYgAzRV7luUMs1jPWATSinp7hplezVKpb7OKvtFREREtKQEyZOvrZIeAHVt4H0pZaQvplTUm5cSsD5f2xxTi4c0GlUfbwM6m9NwX318Gpi5lrR+G7hI0lmU4HuGtuHU840Hzge2p2SXm4uVNBoLbC7pfODwhr4mpXEpFdfHxip8I5m4Cl9EREREy0qQPBmaKum1bZsfWMH2FpTpBj+klKKeU9Lctc1JteRzoxXq4+rAXzs57UQLW0taFtjc9rbAfpT3tC1gHdfQ9FfANpSpD1d30v9BwO22d6zX1dE6gm9SpnwgaSFg7oZ9beftSmW/iIiIiJaTOcndN6xmRt+j3L+jbLuhZPRYYD5J9wGvU+Ykvy1pb+AqSe9RssF/aep3Q0mbAYMppau76u/AfyXdTZmv/BwTKvi9z/Yzkl4D7rD9bif9XQmcKmkH4N/Au5Laq+xxN/CypDuBR4AnOuhraK3CNytweTuV/SIiIiJaTirutYD6ZbcRtkdOqu1knucPwAG2/z4lz9NTqbgXERERfSQV9wIkzQLcAoxs1QA5IiIiopUkkxwtI5nkiIiI6COTzCTni3sREREREU0SJEdERERENEmQHBERERHRJEFyRERERESTBMkREREREU0SJEdERERENMk6ydFKBgO8/fbb/T2OiIiIGMAeeuihhYF/rrDCCh1WIU6QHK1kfoBHH320v8cRERERA9sTwCLAkx01SJAcreQvwJrAc8B7/TyWiIiIGNj+2dnOVNyLiIiIiGiSL+5FRERERDRJkBwRERER0SRBckREREREkwTJERERERFNsrpFtDRJswDnAx8FXgO+YvuFpjbHAusA44Gv2b5L0tzAo8BDtdnltn/WdyPvPZNxD+YFLgRmAZ4Fvmr7f306+F7SxXvwI2ANyr9rp9s+Yxr8PWjvHgyY3wPo2n2o7RYFfmd76fp6mvpdqO2a78GA+V3o4n8PRwEbA+8CB9R/Fz8HXAn8rTY71fbFfTfyySdpOuAU4LPAW8Butv/esH93YE/KdX/P9h8G0nsPPb4H3f43IJnkaHV7AQ/aXhM4Dzi8caek5YFV6s92wBl11+eAi2wPrT9T5f8Mq57egyOBC+tx91H+wZhaTeoerA0santVSpB4sKS5mLZ+Dzq6BwPp9wAmcR8AJH0ZGMH/t3f/sVbXdRzHn4DElV10U0uoTEryTTaIARFLGUQWK8sFuLmC2jB1ruQWS/sxNyNdwly0YTSmieGkZWrezdkyV0oQSlevgsrmq1xbrRUWoWDqBdHbH5/Poe++u+cc7rn3wj33vB7/3O/9fL6f7/f7eZ/P+d73/Z7P+X7hjEJxy4wFqBqDkTQW6r0fZgLzgY+Qzos/zlUzgR8WxkFTJcjZ54C2/F7/NrCuUhERE4EO4HxgEbAmIsYxsl57aCwG/T4HOEm24e4C4KG8/GvS1dKjJD0NLJLUC5wNvJirZgEzI+L3EXFvREw6Xgc8BBqNQc12TaZeXx4HLsvLvaSnN75BC40DqsdgJI0DOLb+vERKkIpaaSxA3zEYSWOhXl8uAB6W1Cvpb8BJEfF20ji4KCK2RcSmiJhw/A550Bztu6SdwOxC3Rxgh6RDkg4ALwDTGVmvPTQWg36fAzzdwoaNiPgysKpU/CJwIC+/ApxabifpSJ5u0AGszMXPA92SfhsRy4AfAZcMyYEPokGOwSn12g1HjcRAUg/QExFjgTtJUw3+GxEtMw5qxKApxwEM6P3wYG5fLG6ZsQBVY9CUY6HBGJwC/Kfwe2WdLuB2Sd0RcR3wXeCaQT/ooVV8HQHejIiTJB3po67S76Z87WtoJAb9Pgc4SbZhQ9ImYFOxLCLuByr/6U8AXq7S9rqIWAvsjIjtwCNAZb5VJ3DDkBz0IBvkGBzM679eq91w02gM8tSC+4Ctktbk4pYaB1Vi0JTjAAb2fuhDS42FKppyLDQYg4OF+uI6nZIq63aSEqVmU+7b6Jwc9lVX6XdTvvY1NBKDP9LPc4CnW9hwtwP4dF7+FLC9WBkRCyOiMtesh/Tx8lvA7cDSXP5xoHvoD3XINBqDmu2aTL0YnAz8DrhD0o2FqlYaB9ViMJLGATTen5YZC0PQbjiq15cdwKKIGB0R7yElUfuA30TEnLxOs46Do32PiLnAs4W6LmBeRLRFxKnAB0hfVBtJrz00FoN+nwP8WGob1iJiPOmj40nAYeALkvZGxM2kK2bdwAbSfKMxwKb8jf73AncAo4BXSd98/eeJ6MNADSAGZ+Z2E4B9ud2rJ6IPA3UMMTif9LHprkKzFflnq4yDajF4jREyDqB+HCR1FdbdK2liXm6Zc0KNGLTMOSHfyWI1KSEcDayS9If8hb4Nuc1e4EpJB09IJxpUuLPDdNJ4XkFKGF+Q9EC+s8OVpH7fJOmXI+m1h4Zj0O9zgJNkMzMzM7MST7cwMzMzMytxkmxmZmZmVuIk2czMzMysxEmymZmZmVmJk2QzMzMzsxI/TMTMzI6L/PS/R4A20v1KbwEmAz8Bpkq6vEq72cBV1err7HMOsFTStxo97rydrcBqSVsHsh0zax5Oks3M7HiZARyWNDs/4GGapHfWayTpSaDfCXJ2HnBmg23NrIX5PslmZlZTRIwC1gKLgSPArZLWR8S5wG3AaaSb83dIeiI/uOBW4CzS0x+/AzwDPAZMJF1NngxMzeXXkK7SLoiIGbnteGA/sAyYUqifAmwETic9KGWlpKcjYjNwAJgFvIv0yNnOvP12YJ2k7xf69BRwhaTuiBgD/BWYCcwHvgGcDIwDLpP0WOVKcm6+WtKCvJ3NpMeAb46ILwFfJ01l7Aa+Kqmn8cib2YnkOclmZlbPJaQn+k0D5gArImIisAW4RdJ0YBVwX0SMA9aTHo89C7iYlPS+Troa/KSki3P5PyTNLu3rZ8CNkqYBdwNfK9XfCXxT0kzSE7XuLtSdBczL2/6BpJeB64EHiglydhfw+by8ENhNehLZVcBnJH0IuJmU4NcVER8ErgA+KmkG8C9S8m9mTcrTLczMrJ75wD2SDgGHgBkR0Q5MkXQ/gKSdEbEfCOBCYGpE3JDbjwXOqbeTiDgDmCTpwbzNjbl8Qf7ZDnwY+GlEVJq1R8TpeflhSb0R8Rzp6nYtPwcej4hrScnyFklvRcRi4LORdrAAeLPecWcfA94P7MzH9jbgqWNsa2bDkJNkMzOr5w3g6Ny8iJgMvNTHeqNIf1fGAAsl7c/rTyJdWZ3Xz/20AcU5y2OAnnyltrLOu0nTMgB6AHKiXHNHkvZGhEiJ8IXA1TkJ7yJdId9Gmqpxdalpb+5nxdjCsd0jqSMfVzv+G2vW1DzdwszM6tkGLI2IsRExHniI9GW4v0TEEoCImEuab/wcac7xV3L5eblsfL2dSDoA/D0iPpmLvkiaW1ys/3NELM/b/kQ+tlqOUD1ZvQtYBzwq6TXgXFISfBPwKLCElPwW7QPeFxFtEXEa/0/8twKLI+IdeQ73RtL8ZDNrUk6SzcysJkmdwA7S9IEngPWS/gQsBzoi4llgA7BE0mFgJTA3Ip4BfgEsl/TKMe5uOXB9ROwCLgWuLdUvAy7P214DXCqp1jfQu/KxrO2jrpM0RWJL/n03sAt4HtgD/Bs4u9hA0h7gV7n+XmB7Lt8NfI/0D8IeUnLd1z7NrEn47hZmZmZmZiW+kmxmZmZmVuIk2czMzMysxEmymZmZmVmJk2QzMyTGOoAAAAAqSURBVDMzsxInyWZmZmZmJU6SzczMzMxKnCSbmZmZmZU4STYzMzMzK/kfjKh6r8MqMKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing Ridge, Lasso and ElasticNet feature selection models side by side for comparison\n",
    "\n",
    "# Ridge\n",
    "# Create a new figure\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['red'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "ax = plt.subplot(311)\n",
    "labels = features\n",
    "viz = FeatureImportances(Ridge(alpha=0.1), ax=ax, labels=labels, relative=False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(False)\n",
    "\n",
    "# Fit and display\n",
    "viz.fit(X, y)\n",
    "viz.show()\n",
    "\n",
    "# ElasticNet\n",
    "# Create a new figure\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['salmon'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "ax = plt.subplot(312)\n",
    "labels = features\n",
    "viz = FeatureImportances(ElasticNet(alpha=0.01), ax=ax, labels=labels, relative=False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(False)\n",
    "\n",
    "# Fit and display\n",
    "viz.fit(X, y)\n",
    "viz.show()\n",
    "\n",
    "# Lasso\n",
    "# Create a new figure\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['purple'])\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "ax = plt.subplot(313)\n",
    "labels = features\n",
    "viz = FeatureImportances(Lasso(alpha=0.01), ax=ax, labels=labels, relative=False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(False)\n",
    "\n",
    "# Fit and display\n",
    "viz.fit(X, y)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having analyzed the output of all utilized visualizations (Shapiro algorithm, Pearson Correlation Ranking, Covariance Ranking, Lasso, Ridge and ElasticNet), we can now select a set of features which have meaningful coefficient values (positive or negative). These are the features to be kept in the model:\n",
    "\n",
    "- Disciplinary failure\n",
    "- Day of the week\n",
    "- Seasons\n",
    "- Distance from Residence to Work\n",
    "- Number of children (Son)\n",
    "- Social drinker\n",
    "- Social smoker\n",
    "- Height\n",
    "- Weight\n",
    "- BMI\n",
    "- Pet\n",
    "- Month of absence\n",
    "\n",
    "Graphic visualization of the feature coefficients calculated in a number of different ways significantly simplifies feature selection process, making it more obvious, as it provides an easy way to visualy compare multiple values and consider only those which are statistically significant to the model.\n",
    "\n",
    "Now let's drop features which didn't make it and proceed with creating models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping features from X based on visual feature importance visualization\n",
    "X = X.drop(['Transportation expense', 'Age', 'Transportation expense', 'Service time', 'Hit target', 'Education','Work load Average/day '], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the features which are going to be further utilized in the modeling stage, might be of a hierarchical type and require encoding. Let's look at the top couple of rows to see if we have any of those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month of absence</th>\n",
       "      <th>Day of the week</th>\n",
       "      <th>Seasons</th>\n",
       "      <th>Distance from Residence to Work</th>\n",
       "      <th>Disciplinary failure</th>\n",
       "      <th>Son</th>\n",
       "      <th>Social drinker</th>\n",
       "      <th>Social smoker</th>\n",
       "      <th>Pet</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Body mass index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>90</td>\n",
       "      <td>172</td>\n",
       "      <td>30</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>178</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>89</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>36</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>172</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month of absence  Day of the week  Seasons  \\\n",
       "0                 7                3        1   \n",
       "1                 7                3        1   \n",
       "2                 7                4        1   \n",
       "3                 7                5        1   \n",
       "4                 7                5        1   \n",
       "\n",
       "   Distance from Residence to Work  Disciplinary failure  Son  Social drinker  \\\n",
       "0                               36                     0    2               1   \n",
       "1                               13                     1    1               1   \n",
       "2                               51                     0    0               1   \n",
       "3                                5                     0    2               1   \n",
       "4                               36                     0    2               1   \n",
       "\n",
       "   Social smoker  Pet  Weight  Height  Body mass index  \n",
       "0              0    1      90     172               30  \n",
       "1              0    0      98     178               31  \n",
       "2              0    0      89     170               31  \n",
       "3              1    0      68     168               24  \n",
       "4              0    1      90     172               30  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like 'Month of absence', 'Day of week' and 'Seasons' are not binary. Therefore, we'll be using pandas get_dummies function to encode them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding some categorical features\n",
    "X = pd.get_dummies(data=X, columns=['Month of absence', 'Day of the week', 'Seasons'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance from Residence to Work</th>\n",
       "      <th>Disciplinary failure</th>\n",
       "      <th>Son</th>\n",
       "      <th>Social drinker</th>\n",
       "      <th>Social smoker</th>\n",
       "      <th>Pet</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Body mass index</th>\n",
       "      <th>Month of absence_0</th>\n",
       "      <th>...</th>\n",
       "      <th>Month of absence_12</th>\n",
       "      <th>Day of the week_2</th>\n",
       "      <th>Day of the week_3</th>\n",
       "      <th>Day of the week_4</th>\n",
       "      <th>Day of the week_5</th>\n",
       "      <th>Day of the week_6</th>\n",
       "      <th>Seasons_1</th>\n",
       "      <th>Seasons_2</th>\n",
       "      <th>Seasons_3</th>\n",
       "      <th>Seasons_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <td>172</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>178</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>51</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>89</td>\n",
       "      <td>170</td>\n",
       "      <td>31</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>168</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>172</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Distance from Residence to Work  Disciplinary failure  Son  Social drinker  \\\n",
       "0                               36                     0    2               1   \n",
       "1                               13                     1    1               1   \n",
       "2                               51                     0    0               1   \n",
       "3                                5                     0    2               1   \n",
       "4                               36                     0    2               1   \n",
       "\n",
       "   Social smoker  Pet  Weight  Height  Body mass index  Month of absence_0  \\\n",
       "0              0    1      90     172               30                   0   \n",
       "1              0    0      98     178               31                   0   \n",
       "2              0    0      89     170               31                   0   \n",
       "3              1    0      68     168               24                   0   \n",
       "4              0    1      90     172               30                   0   \n",
       "\n",
       "     ...      Month of absence_12  Day of the week_2  Day of the week_3  \\\n",
       "0    ...                        0                  0                  1   \n",
       "1    ...                        0                  0                  1   \n",
       "2    ...                        0                  0                  0   \n",
       "3    ...                        0                  0                  0   \n",
       "4    ...                        0                  0                  0   \n",
       "\n",
       "   Day of the week_4  Day of the week_5  Day of the week_6  Seasons_1  \\\n",
       "0                  0                  0                  0          1   \n",
       "1                  0                  0                  0          1   \n",
       "2                  1                  0                  0          1   \n",
       "3                  0                  1                  0          1   \n",
       "4                  0                  1                  0          1   \n",
       "\n",
       "   Seasons_2  Seasons_3  Seasons_4  \n",
       "0          0          0          0  \n",
       "1          0          0          0  \n",
       "2          0          0          0  \n",
       "3          0          0          0  \n",
       "4          0          0          0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Distance from Residence to Work', 'Disciplinary failure', 'Son',\n",
      "       'Social drinker', 'Social smoker', 'Pet', 'Weight', 'Height',\n",
      "       'Body mass index', 'Month of absence_0', 'Month of absence_1',\n",
      "       'Month of absence_2', 'Month of absence_3', 'Month of absence_4',\n",
      "       'Month of absence_5', 'Month of absence_6', 'Month of absence_7',\n",
      "       'Month of absence_8', 'Month of absence_9', 'Month of absence_10',\n",
      "       'Month of absence_11', 'Month of absence_12', 'Day of the week_2',\n",
      "       'Day of the week_3', 'Day of the week_4', 'Day of the week_5',\n",
      "       'Day of the week_6', 'Seasons_1', 'Seasons_2', 'Seasons_3',\n",
      "       'Seasons_4'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation and Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our matrix of features X is now ready to be fitted to a model, but first we need to split the data into train and test portions for further model validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform 80/20 training/test split\n",
    "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purpose of model evaluation and selection we will be using Yellowbrick's Classification Report Visualizer, which displays the precision, recall, F1, and support scores for the model. In order to support easier interpretation and problem detection, the report integrates numerical scores with a color-coded heatmap. All heatmaps are normalized, i.e. in the range from 0 to 1, to facilitate easy comparison of classification models across different classification reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to visualize estimators\n",
    "def visual_model_selection(X, y, estimator):\n",
    "    visualizer = ClassificationReport(estimator, classes=['Low', 'Medium', 'High'], cmap='PRGn')\n",
    "    visualizer.fit(X, y)  \n",
    "    visualizer.score(X, y)\n",
    "    visualizer.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_model_selection(X, y, BaggingClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_model_selection(X, y, LogisticRegression(class_weight='balanced'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_model_selection(X, y, KNeighborsClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_model_selection(X, y, RandomForestClassifier(class_weight='balanced'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_model_selection(X, y, ExtraTreesClassifier(class_weight='balanced'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of this exercise we will consider F1 score when estimating models' performance and making a selection. All of the above models visualized through Yellowbrick's Classification Report Visualizer make clear that classifier algorithms performed the best. We need to pay special attention to the F1 score for the underrepresented classes, such as \"High\" and \"Medium\", as they contained significantly less instances than \"Low\" class. Therefore, high F1 score for all three classes indicate a very strong performance of the following models: Bagging Classifier, Random Forest Classifier, Extra Trees Classifier.\n",
    "\n",
    "We will also use Class Prediction Error visualizer for these models to confirm their strong performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizaing class prediction error for Bagging Classifier model\n",
    "classes = ['Low', 'Medium', 'High']\n",
    "\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['turquoise', 'cyan', 'teal', 'coral', 'blue', 'lime', 'lavender', 'lightblue', 'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue'])\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "ax = plt.subplot(311)\n",
    "visualizer = ClassPredictionError(BaggingClassifier(), classes=classes, ax=ax)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(False)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "g = visualizer.show()\n",
    "\n",
    "# Visualizaing class prediction error for Random Forest Classifier model\n",
    "classes = ['Low', 'Medium', 'High']\n",
    "\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['coral', 'tan', 'darkred'])\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "ax = plt.subplot(312)\n",
    "visualizer = ClassPredictionError(RandomForestClassifier(class_weight='balanced'), classes=classes, ax=ax)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(False)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "g = visualizer.show()\n",
    "\n",
    "# Visualizaing class prediction error for Extra Trees Classifier model\n",
    "classes = ['Low', 'Medium', 'High']\n",
    "\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['limegreen', 'yellow', 'orange'])\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "ax = plt.subplot(313)\n",
    "visualizer = ClassPredictionError(ExtraTreesClassifier(class_weight='balanced'), classes=classes, ax=ax)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(False)\n",
    "\n",
    "visualizer.fit(X_train, y_train)\n",
    "visualizer.score(X_test, y_test)\n",
    "g = visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can conclude that ExtraTreesClassifier seems to perform better as it had no instances from \"High\" class reported under the \"Low\" class.\n",
    "\n",
    "However, decision trees become more overfit the deeper they are because at each level of the tree the partitions are dealing with a smaller subset of data. One way to avoid overfitting is by adjusting the depth of the tree. Yellowbrick's Validation Curve visualizer explores the relationship of the \"max_depth\" parameter to the R2 score with 10 shuffle split cross-validation.\n",
    "\n",
    "So let's proceed with hyperparameter tuning for our selected ExtraTreesClassifier model using Validation Curve visualizer!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UPB4Pt956KzfffDNtbW0ALF26FF3X9/j+KBQKhWLvMA2T6I6oLa7aG9ppq2+jrb6NyJYInds6ycQzfR7rcDsIVYUorS0lWBkkVBUiUBnAX+ZH0zT+sfQftlXI6XXy2Yc+S9GEItAKQjh6LtPP9r1dzq33tY/D4ZBlu1vOGcDs7eTq0WDS8ZPYsmoL6Uyak64/aU9v/35jzIivkUYqleLJJ58E4Je//CW//vWv8fv9fPvb32blypVUVVXZ+27ZsoUHH3yQmpoaLr74YtasWdOtrk2bNvHb3/4Wv9/PggUL2LVrF/fddx+nn346X/jCF1i1ahWrVq2iJ/feey8PPPAA3/zmN2ltbeXiiy/miiuu4N577+W0005j4cKF/POf/+S9997jww8/5IQTTuDyyy+nsbGRhQsX8uKLLxKPx1m0aBGzZs3i9ttv57jjjuOSSy5h06ZN3HjjjTz22GNDeyMVCoViBCGeETRuaIR5Q1N/NpmVLsGGdjo2d0jr1cY22jdKsRXdGcXM9j2IyBPyEKoJEawKEqwMEqwIEqgIEKoOEZ4QJlQVsi1SDrcDp9eJJ+DBE/bg8rrIxDKsuHUFACfdeBL62WPvz7XLK12NGzZsGNa47zEjvs64/YwBrVRt9W384rBf2NYvl8/FovcXDZnJcdq0afZyWVkZ119/PcFgkPr6eo488shu+5aWllJTUwNATU0NqVT3CdAnT55MKBQCoKKiglQqxYYNGzj//PMBOProo3udv6Ojg+3bt3Pttddy7bXX0tjYyFVXXUVdXR0bN27kggsuAOD4448H4M9//jPnnHMOAFVVVYRCIVpbW7tdy0cffcRrr73Gs88+C0AkEtmHO6RQKBSji31N0GlZFsn2JB0NHV0CK2e9at8kl+O74n0frIG/zE+ZXmaLqmBlkFBNiKIJRRRNKsJf5sfpdkq3n9eFJ+zB7Xfj9DgH5fY76YaTePehd9E0bVitQkONfo5OdHV0WNswZsTX7iitLeXE60+0Vf1Q+3odDmn37Ozs5K677mL58uUAXHHFFd391ex+RF5f5TNmzODtt99m5syZvPPOO73K0+k03/jGN3j00UepqamhoqKC8vJyPB4PdXV1rFmzhkMPPZQ333yT5cuXU1dXx1tvvcWsWbNobGwkEolQUlLS7Vpqa2s599xzOeecc2hpabEtewqFQnEwsLtBW5Zp0bmj07ZY5cVVx+YOKa42d5Du7HuGEYfbQbAySPWR1dJaVRUiVBMiPDFM8aRiiicX4w5KIeX2u6Ww8rlxuPbfLIEun4uz7jwLTdNUNoAh5qC6u8Oh6kOhEHPnzuX8888nEAhQVFREU1MTEydO3Kd6v/KVr3Ddddfx7LPPUllZ2SvnWUVFBUuXLmXx4sW4XC4Mw+CUU07h+OOPZ+7cudx000386U9/AuCHP/wh4XCYm266ieeff55kMsl3v/vdXnVeeeWV3HzzzTzxxBNEo9F+R2oqFArFWKPnoK1XfvAK8ZY4mc5Ml8ja0oGZ6dsl6A66CVYEqZhVQbAqSHh8mPCEMEWTiiiZXEKoJoTb78YddOMNeaW1ynHgU+Xo54w9V+NIROtphRmJrF69eiqwcfbs2fYs5Pn5Cfc0W/tYSbK6YsUKSktLOfzww3n11Vf55S9/yUMPPbTb42Kx2EE12nFvPycjmdWrVzNv3hAFnCiGHNV/I5dsKmtbqfKvjga5vn31doxk/3mp/OP8BMoD0lpVIKyKJxdTOq0Uf5kfT8iDJ+DZL6P/FPvGUH8PU6kUa9euBZg2b968TT3LDyrLF4wdVT9x4kRuuukmO4P7zTffPNxNUigUihFNfoqZjoYO2ja00VrfSlt9Gx2bOohsjRBrivV5nObQ+nTvVR1RxVk/O4viacUEy4MyrcJuck0pFHAQiq+xQl1dHX/4wx+GuxkKhUIxIjCzJslIkmbRTNv6NjvWKrI1QmRrhM7tnf1mN9ecGsGqXLxVpYy3KppURMmUEkprSymZVkImmuE3x/2m26Cti/540bDliVKMbpT4UigUCsV+Y3+Fdthz/+WmrzGzphwpuLlLVHVu65SvHZ3EGmMk2/tOHupwyWD2mrk1hKplKgZ/mZ9wTZjiKcUUTS7CG/LiDsgpaTxBT5/xVgdy0JZibKPEl0KhUCj2CwPNn2sacoLkbCJLOpbGzJqYmdykyVkDM2OS6kjRsVUKq+iOKLHGGPFdcaKN0YHFVT556PRSOUqwOkSoKkSwOkigLIBvnA9vWIord8CNy+8iUBbAHXDvUexVftBWOj28CToVo5+DTnw988wGNA3OPrtuuJuiUCgUY4oV319hp2J49uvPcuQVR2KkpbAyDZN0LE28OU58V5xYU4zoziixnfI9ujNKKpLqs16H20GoOsS46eOksMpZr/LLvhIfpmHicDqkuArIaW08QQ/+Mj9u/56JrP7Ip2IY7gSditHPQfXpSSazXH3139E0WLBgCj715VEoFIp9Ih1NE9kWoXFNI/+8/Z/29rfvf5vI1gjpaFqKrMZYv+LK6XESqg5Rfmh5N1FVKK40hybn70sbOFwO3EE3br+0YnlCHnylvv0msgZiJCToVIx+Dir1sWzZ62zc2AHAbbe9wXe+c8I+1/nxxx9z++23k0gkiMfjnHzyyVx11VVD9gNw3XXXceyxx9oZ6gEeeOAB2trauOaaa/o85sQTT2TVqlX84Ac/4KKLLmL69Ol22YYNG7jlllv4/e9/3+85H374YS699FJefvllduzYwUUXXbTX7V+xYgX3338/DocDwzC44IILOPfcc/e6PoVCcWCxLItEa4LoziiJ1gSZRIb2+nZWfG+FPS8ggGVYrH92PSAtRsGqIOUzy7vcggXWK1+pr9tvpmmYmFlTiqxATmQFcpascX5cPpdK1aAY1QyZ+NJ13QH8AjgCSAFfFkKsLyhfAiwETOCHQog/DlVbAOrr27nttjft9WXL3uCyy2YxbVrJXtcZiUT45je/yd13383UqVPtCakff/xxFi5cuD+a3YsLL7yQO++8s5v4+uMf/8g999yz22NvvvlmYrG+h1IPxL333sull17KJz/5yT0+tie33HILTz/9NEVFRUSjUc477zxOPPFEysrK9rluhUIxNFimRawpRqwpRrwljpExaNvQRsOKBhpWNBDd2bclqHJOJad+91S8xd4+xZJpmFiGBSY4gzJzuzvgxhPKiSzvQWUfUBxEDOUn+7OATwhxvK7rxwF3AOcB6LpeAnwdmA4EgXeAfRJf1167nCef/Kjf8qamOMncEGGQLsjDDnuAyspAv8d8/vMzuP32U/otf+mll5g/fz5Tp04FwOl0ctttt+F2u3n99df58Y9/jNvt5sILL6SiooKf/exneL1eSkpK+OEPf0g2m+Ub3/gGlmWRyWS49dZbmTp1KldffTXRaJRkMsm1117L/Pnz7XMeffTRtLa2sm3bNiZMmMB7771HeXk5EydO5KOPPmLZsmWYpkkkEmHp0qXMnTvXPvY//uM/uP7666msrGTJkiVYlkVFRYVd/txzz/HII4/Y63feeSd/+MMf6Ojo4JZbbuHwww+nvr6eJUuWcP/99/OXv/wFl8vF0UcfzbXXXsvdd9/N1q1baWlpYfv27dx444184hOf6HbPysrKeOihhzjzzDOZPn06zz77LB6Ph5aWFm644QY6OzuxLIvbbruNcePGce211xKNRm1he/zxx3P22WczdepUPB4Pt956KzfffDNtbW0ALF26FF0fG7ncFIrhxDRMOrd3Et8VJ9GawMKi9aNWNq3YRMOKBmKN8o+cO+CmdkEtlXMqefOeN23rl9Pj5KQbT7LjsYysgdPtlBasgEe6C8MeAuMCOD3O4bxUheKAM5Ti6yTgOQAhxGu6rhfO/hwDGpDCK4i0fo06mpqamDRpUrdthdnjU6kUTz75JJZlcfrpp/PYY49RVVXFgw8+yL333sv8+fMJh8PccccdrF+/nmg0yubNm2lubuaBBx6gpaWFTZs29TrvBRdcwJ/+9Ce++tWv8tRTT3HxxRcDsH79eq6//np0XeeZZ57hqaee6ia+8vzud7/j7LPP5sILL+Svf/0rjz32GACbNm3i17/+NX6/n29/+9usXLmSr371qzz88MPccsstPPXUUwAIIXj22Wd5/PHHcblcXHXVVfzjH/8AZCb53/zmN6xatYr777+/l/i69957eeCBB/jmN79Ja2srF198MYsXL+bee+/ltNNOY+HChfzzn//kvffe48MPP+SEE07g8ssvp7GxkYULF/Liiy8Sj8dZtGgRs2bN4vbbb+e4447jkksuYdOmTdx444329SgUij0jm8oS2RYh0ZIg2Z5Ec2i0iBY2Ld9Ew8sFgivopvZTtUw5eQoTjplgi6dEa4J3H3wXgMMvO5zK2ZW4/C58RT58pT6cbiWyFAoYWvFVBHQUrBu6rruEEHnz0xbgA8AJ/GgwFeZS9dvU1dWRyWQAuOWWY7jllmP6PXbjxgjHHPM4ydz0ED6fkzffvIipU4sGPOdAbrqysjLWrVvXbZ9t27axc+dOLMti8uTJxGIx2traCAQChEIhYrEYs2fPZvny5SxatIiPPvqI//qv/8LlcvGlL32J8ePHc8EFF3D11VeTzWa5+OKLe7XhU5/6FFdeeSUXXXQRr732Gtdccw2xWIyioiLuvvtuvF4vsVjMPp9lWcRiMQxDXvu6des444wziMViHHrooRiGYe+/ZMkS/H4/mzZtYubMmd2OT6VSZDIZPvzwQw477DDS6TTpdJo5c+bwwQcfkE6nqaurIxaLUVxcTCKR6Nb2SCRCQ0MDixYtYtGiRTQ1NbFkyRLq6upYv349n/nMZ4jFYhx++OEAPP300yxYsMBuWyAQYOvWrZimSWVlJbFYjA8//JBXX32VP//5zwC0t7d3O2cmk2HDhg0D9vFoZPXq1cPdBMU+MJL6LxvPktyVJN2RJhvLggNim2K0rG6h9e1W0m1yii6n30n5ceWUzS2jeGYxDreDBAnWb1yPZVg4nA7Cp4fx/82P5tCovqKaremtkEY+CbYM62Xud0ZSHyr2juHsw6EUXxEgXLDuKBBenwZqgGm59ed1XV8lhHhjoAr3ZW7H2bODXH/9sdx6qxyNc8MN8znssJrBXUk/nHXWWbaFavLkyWQyGe68805OOOEEpk+fjtvtJhgMEggEiMfjxGIxKisrWbNmDXV1daxdu5aJEyfy4IMP8vbbb/OTn/yEpUuXks1m+e1vf0tTUxMXX3wxn/70p7udNxgMcsghh/Dggw9y5plnUlxcDMAdd9zBj3/8Y+rq6rjrrrvYtm0bwWAQTdMIBoM4nfJf5yGHHMK6des46qij2LBhgz1F0a9+9SuWL18OwBVXXIHH47EtecFgEK/Xi9vtZubMmTz66KN4vV6cTifvvfcen/3sZ1m3bh1er5dgMIjf78fpdHazBMbjcW688UYeffRRampqmDJlClVVVRQVFTFjxgzWr1/PUUcdxZtvvsny5cuZMWMG77//PvPmzaOxsZFoNMr48eNxOByEw2G8Xi+HHHIIs2fP5pxzzqGlpYUnn3yy2znz4lDN7agYKYyE/ku0Jejc0WkHzDtLnOzatku6FF9uIL4rDkgLV92ZdUw9eSo182q6uQdNw0RDw1/uJzw+TKA8gKZpTCyZKJOsnjC6588diJHQh4p94wDO7dgnQym+VgHnAE/kYr7WFJS1AQkgJYSwdF1vB/Y+8n2Q3HDDfB566AM0Da6//th9ri8UCrFs2TKWLl1qW4dOPfVULrnkEt54o0tHaprG97//fXsUZHFxMT/60Y/QNI1rrrmGBx98EIfDwde+9jWmTp3KPffcw//93//hdrv5+te/3ue5L7zwQr7yla/w3HPP2dvOPfdcFi1aRFlZGdXV1XYcVE+uvvpqrrnmGv76178yceJE+1rmzp3L+eefTyAQoKioiKamJkBaGJcsWcIJJ8jRobqu8+lPf5qFCxdimibz5s1jwYIFrFu3bsD7VVFRwdKlS1m8eDEulwvDMDjllFM46aSTmDVrFjfddBN/+tOfAPjhD39IOBzmpptu4vnnnyeZTPLd734Xl6v7R/bKK6/k5ptv5oknniAajbJ48eIB26AKfbCHAAAgAElEQVRQHIxYVveAeTMtRxI2vd9Ew/IGKbiapeDyhDzUnVUguApchZZpYZom/nF+QtUhwtXhXpngx8r8uQrFUKJZljUkFReMdjwc0IArgH8D1gsh/qTr+q3AWch4r5XAdUKIPhuzevXqqcDGfbF85TnYk6zGYrFulqGxzt5+TkYy6l/36OZA9Z9pmER3RGVS05Y4WIAGTWub5CjFlxvsuQ49YQ+TT5rMlJOnUDO3h+CyLMysia/ER6gqRHhC+KCfPFp9B0c/B9DyNW3evHmbepYPmeVLCGECV/bYvK6g/DvAd4bq/P1xzjkHp+hSKBRjHyNj0Lmtk3hznERbwrZKNa1pYtOKTWx+eTOJVim4vEVeDvm3Q2zB5XB1CSrLsjAyBr5iH4GKACWTS7qVKxSKfUMlUVEoFIpRTCaesUcopiIpHG4Hlmmx6/1dbFq+ic2v9BBcn8kJrqNqegmqbCqLN+wlUBGgaFIRbp97OC5JoRjzKPGlGNNYlqUyYSvGHMmOJJ3bcwHzsQxOjxPTMLtciq80kGyTk1B7i73MOHsGU06eQvWR1b0El5ExcHldBCoCFE8uxhMcOy56hWKkMmrFl8PhIJ1Oj6lYHsX+xzAM9RlRjHosyyLeHCfWmMswn5YJS03D7HIpvrKZZLsUXL4SHzPOmcHUk6dSdWRVrxgtIyvnRwyWBymaWISvxDccl6VQHLSMWvHlcrns+RSdTqeybgySTCZjB6GPZSzLwjAMDMPoNUJSoRgNWKZF586uDPOmYeJ0ScHV+F4jDcsb2LyyQHCV+phxbk5wHdFbcNmpISr8hGu6UkMoFIoDz6h+KoXDYbLZLKY5KhPkDwsbNmxgzpw5w92MIUfTNDwejxJeilGFmTWJbI0Qb8kFzGsaDqeM4Wp8p1HGcK3cTKojBUjBpZ+rM+WUKVQd3ltwFaaGCNeECVWHlOBSKEYAo/7JpB6ue45ywykUw4N4RtC4oREKRrhnkhkiW3NT+nQkcbqlJV9DY8fqHTS83MDmVzaTikjB5R/nR/+sztSTp1I5p7K34LIszIwUXMGqIOHxKjWEQjHSUMpFoVAoDgDZZJbnrn6OdDrNJy/5JInWhAyYj2bszPEOh4Ptb25n0/JNbFm1pUtwlfk59PxDmXLyFCpn9yO4sibeIi/ByiDFk4pVagiFYgSjxJdCoVDsA5ZlYRkyL1Y2kSUdT2Nl5bqZMTGy8v2te9+ifWM7AC8seYGj/t9RsgINtr6+VcZwrdpMulPGZPrLpeCaespUKmdX9sokD2CkDTxhD4FyOVLR5VU/6QrFaEB9UxUKxYhBPCPkvIBnD8+8gHkLkpE2yCQyZBNZzKxpb8svm5nctozcZplycg6H04Hm1HrFVXVu7+S9379nr7//h/cJVYVoXNvIlpVbSEel4AqUB6g7Q07tU3FYRd+CK2Pg8uVSQ0xSqSEUitGIEl8KhWJEkHfLaZpG7YJaXL59+3nqT0gZaaObeLIMa4+EVCGaptlT8eRjrdLRNOlYmkw8QyaWIZPI8M7972CkDfs4I23w6o9fBSBQEbDnUqyY1Y/gysrUEoHyAEUTVGoIhWK0o8SXQqEYEaxcttJ2y628bSWnfOcUuywvbIyMQSaeIZvM2m69nlaovGXKyBpyPkMGFlKmYUp3YV4w5UVTgXjKxDKk4+lu6z3LM/EMZnbwI69DNSE+cfMnqJjZt+AyDRPNoREoDxCuCeMv86uRigrFGEGJL4VCMaxYlkXTB02sXLbS3rbyRyupnF1JoCKAlZE522wh5XKABkbK6FMA2e/xnGDqsa2nwMoms3vXcA3cATfugBtfqY+iiUW4Ai48AY/cHnTb5Uba4L3fv2eLM6fHyRl3nEG4JtytStMwsSyLYHmQUHWIYFVQCS6FYgyixJdCoTggWKZFOpYm0SanxMkHp2diGV664SWMVIFbLmXwl6/+hYpZFb3EVTqWJpvI2u7BPcXpdeIOuPEEZaB6XiC5g27c/gLRVLDuCXi6iSl30I3L6+rTYjXQ9b/74LsAzL5kti287NQQZX6ClUGKJhTtUb0KhWL0ocSXQqHYr1imRaozJUVWPEM2niWTyJBoTdCxtYPojiidWzrp2NJBx+YOIlsj3YRXnviuOA0rGgDQnBqeoLQohapCvSxLPdd7iaWC8uHKeTXnkjls+NsGMpkMsy+ejZE28JX4pOCaWKRSQygUBxFKfCkUir3CNExSkRTJ9mSXmy+WJtIQIbItQueOTiJbIlJgbYkQb473qsPpdVI0sYhARYDtb27HMnLB7m4HC25bQMnUEtwBN07P8E4hZpmWTClhWmDRZXXT8m8aOGQAvqZpMr7MUfDSNFx+F8d/63i2bttKaV0pxZNUagiF4mBFffMVCsWAmFmTRHuCVCQlrVjxDLHmGG0b2ujc1knn9k5bYEW29W3FClYGqZlXQ9GkIoonFVM8uZiiSUUEK4K2i+2dB96x3XJzvjCHmqNqAGzR0+3dsrqJMQ0NNGyxU7jscDrkvg66C6KcKMrv73A6epXn63E4HThc3V+96hqEq3Di/ImsXr2asull+6NrFArFKGVQ4kvX9anAYcBzwGQhxMahbJRCoTjwGBmDZFuSZCRJNpEl1ZmifVM77Rvb6dzWSWRbxLZkJVoSvY53+Vy2qCqeXEzxJLlcNLEIt9/da3/TkGkfNKeGy+fiqK8cRf2L9WgOjROvPxG3z20Lpp7Cpy+hpALTFQrFaGG34kvX9YuApUAAOB74p67rS4QQDw914xQKRXf6mhtwT8mmsiTaEqQ7ZeB6tClK60ettG3KWbK2ddqxWGamd+qEYFWQ8UePt61YRZOl2AqUB3oJINPoSgPh8rlw+V24fW6cPieeoAd/qR+nt8ul+Jl7P4OmaVQcWrH3F6hQKBQjnMFYvq4HTgBeFkI06bp+FPAioMSXQnEAKZwb8IwrzxgwCallWRgpg3hLnHQsTbozTVt9G60ft9LekLNkbZWWrERrH1Ysv4vSaaVdAitnzSqaWNTrvJZp2Tm28iMJ3T63FFoBN74SHy6fa1CWKf0cfc9vjEKhUIwyBiO+DCFEp67LH0UhxA5d1wefSVChUOwX+kpCalkWmUSGZKsMeu/c2UmLaKH141Y6NnfQub3TjsXqZcXSIFQVYvwx47ssWDmh1dOKlRdYmkPGTrn9Mg1D3oLlLfbi9ruV60+hUCgGwWDE1/u6ri8G3LquHwksAt4Z2mYpFIpC2urbWHXbKnt95Q9XkolnSLYnpbjaKl/JtmSvY90BN6W1pV3xWAWxWIWj7SzTIpvO2sHlLp/LfnmCHrwlSmApFArF/mAw4utryJivBHA/8HfgW0PZKIVCIckkM3Ru7+R/Pv8/3TKxG2mDV//71a4dNQhVh5gwf0KvEYX+cV3T0tgWrFzqA5dXxmG5fC47U7sSWAqFQjG0DEZ8/VwIcQVw41A3RqE42LEsi3hLnFhjjJaPW1j/l/VsXL6RtvVtvfYtmlTE3C/NpWhyEUUTinB6chM8FwqsvPXK78Ltl3FYSmApFArF8DIY8TVb1/WQECI65K1RKA5CjIxB57ZOEq0JIlsjbFy+kYZ/NLDz3Z1gyezu1XOraXqvqdvcgKd+71TCNWFcPldXoHteYJXkBJaapkahUChGHIMRXyawWdd1gXQ9AiCEOG3IWqVQjGEsyyLZniS6M0qyPUmiJcH2t7az8e8b2fraVjswvvLwSmpPr2XKyVPwFfv412//xZqH1wBwzOJjmHXBLCWwFAqFYhQyGPF13ZC3QqEY45hZk8g2GRCfaE2QTWfZ9f4uNr64kYZXGsjEMgCUTCuhdkEt006bRqg6JCddNkx843yc+ZMz2bJqC+l0mtN/cPqAqSYUCoVCMXLZ7a+3EGKFruufBk7P7f8PIcTTQ94yhWKUk4wkie6I2lnjHU4HbevbqH+xno3/2GhniQ9WBtHP1aldUEtpbSkARlZmfi8aX0TJlBJ70uWz7jyLDRs2KOGlUCgUo5jBZLi/Dvgc8AhyGtmbdV2fLYT4wW6OcwC/AI4AUsCXhRDrC8o/DXwnt/ov4GtCCGuvrkKhGAGYhkl0R5REa4J4axwzY+J0O4lsi7DxxY3Uv1RPZEsEAG+RlxnnzKB2QS2Vsytt16GRMfCV+iifXE6oMtTrHPo5OtHVKvxSoVAoRjOD+ft8KTBfCJEA0HX9PmA1MKD4Aj4L+IQQx+u6fhxwB3Bero4wcDtwihCiOSfwyoFde3cZCsXwkI6l6dwug+VTkZQ9CXOyNcmmf2yi/sV6mtc1A+D0Opl66lRqF9Qy/pjxON1ydKJpmGDKVBEl00r6nAdRoVAoFGOHwYgvR1545UgC2f52LuAk5ETcCCFe03X96IKyE4A1wB26rtcCvxFCKOGlGPFYpkWsKUa8OU68NU42mcXlkV8jM2Oy+ZXN1L9Yz45/7cAyLTSHxvhjxlO7oJbJJ03GHegSVkbGwBPyUDqhlKKJRSpwXqFQKA4SBiO+XtJ1/X+BB3LrX0QmWt0dRUBHwbqh67pLCJFFWrlOBY4EosAruq7/Uwjx0UAVrl27dhCnVeyO1atXD3cTRhXZZJZUc4p0JE22U/7v0JxSKJlZk44POmh+o5m2d9vskYrBqUHKjy2n7OgyPEUesmSp31KPZVlggqfUg7/aj8fjoWlX0x7bfFUfjm5U/41+VB+OfoazDwcjvr4BXAlcBjiAl4BfD+K4CBAuWHfkhBdAC/CmEGIngK7rLyOF2IDia/bs2Xi93kGcWtEfq1evZt68ecPdjBGNZVnEm+PEmmIk2hJk41kcRQ604q4s8U1rm6h/sZ6GFQ2kIilAJj2tPb2WaQumUTShqFudpmHicDkIjw9TOq3UDqDfG1Qfjm7Gev8988wGNA3OPrtuuJsyZIzlPnz66fWYpsXZZ9ficjnGbDLmoe7DVCo1oMFoMOIriBROn9d1fQLwX4CH3bseVwHnAE/kYr7WFJStRiZvLQfageOA+wbRFoViSMimskR3RIm3xEm2J8HCFkj5zPFt9bmRin/fSKwxBoC/zM+sC2ZRu6CWcTPG9fqhMjIGvhIfxZOKCVYFx+wPmUJhmhbbt3eyaNELAIwfHyQY9OByaTidGk6nA5/Phc/nwu124HI5cDo19Z0YIgzDJJMxSaWyJBJZUimDbNbMvSy7vGubSSyW4cor/yZnx3B9Cp/PhcvlwOOR/ZXvN7nNicvlwOdzEgi48XqduN1OHKMgfOKZZzawYUMzw6mfByO+HqVLOHUirV+/R46AHIg/Ap/Sdf1V5CjJK3Rd/yawXgjxJ13XbwSez+37hBBC+RQVBwzLski0Jog2ykSn6Wgap9uJpsmA+TzRnVE2/n0jG1/aSFu9nOLHHXQz/azp1C6operIqm77g7SMWZZFqCpEaW1ptzgvhWKsYJoWra0JmpsTdHSkiURSPPTQ+2zdKkfj3n3321x++WH2/pZlYZoWhiHfNU3D4ZCizOXSciKtS5TJ9/zDXgo4j8eJz+fC63XaImAsizfLsmxhlEhkSSYN0mmjQDhZZLMG2axFJiPFVSZj2vfYsqzcPZb3dnc88YRg5844AE899bHdf5mMrDeR6H2MYcjzWblcBbIvnXg8DtxuZ4Fg0+z1YNCN3+/C7Zb9eCBJJrNcffXfSadTXHnl6fiGKW3PYM46RQhxLoAQIgIs1XX9nd0dJIQwke7KQtYVlD8OPL4HbVUo9gkjY9gjE5NtSUzDtEcc5oPmAZIdSRpWNFD/Uj1N7zUB4HA7mPyJyUw7fRoTj5uIy9v7q2NkDDxBD+HxYYonF6sAesWYoqfY6uyU7vb8w3PXrjhPPNEVOfLoo+s444wp1NTIlCmalhcBfdedTlvICVX6J/+gN03QtMI6u4u3/KunsHO7nfh8TttKM1hRUsjeWE1M08oJI4NEQlqipIDqEk2GYeWEk5nbV1qnpIgCpxOcTsduLUsOh7ZH1ifDMInHs9TXd/Doo/Yjmkce+ZDp00sYPz6ExyPvmcfj6GXhcjodvfrUsixSKYNUyuh1PsuSAjybtdA02V63W1rVnE5pUXO7Ndu6Ji2mToJBT+7c+ya4ly17nY0bZTj6bbe9wXe+c8Je17UvDEZ8WbquzxFCrAHQdX0mkBnaZikU+4dkR5LOHZ0k25OkIimcLieaQ0NzaDgdXb8Y2WSWLa9uof6lera/sV3OoahB9VHVTDt9GlM+OQVvuHe8oWVZmFmTQHmA4inFBMYFDuTlKRRDhmlatLQkaG6O09GRJhpNA11iq6fF4o473iKd7nrYptMGN974Cueffwherwu/34nX68Lnc+L3S+uVdEPKd49n9y6rvh70efJWoL4e+IXXJK1vJtIho+Fw0M3alrfSFFre8uIukzFZvPhFMpkM558/H9ByQqLQCmXaIiqdzoupfApLK3cNu3e3OhzgcPQtDC3LIpnMEotlicczxONZYrGMvRyPZ3LrA5VnSSQyJJN9369MxmTp0lX9ts/tdhSIsi5hJped3QRb9/2679vzmL7K8n3j8ThwOp05K2jeFerMiTdnzromhVwgIK1reQEHUF/fzm23vWlfw7Jlb3DZZbOYNq1kwL4YCgYjvpYAL+i6vhWwgEpk7i+FYkRgWRZY8t0yLaI7ZaLTRGtCJjr19LZugQyC37F6B/Uv1rN55WayCRnGOG76OKYtmMa006YRrAj2eU47gL46TEltSa+6FYrRRk+x1dmZRtP6FluGYbJpU4S1a5tZu7aZNWuaaWyM96qzoaGTn/3sX4Nug7RKdQmynu89BVvP7X2JusJ98pajvlxd0vpmdBOQPfnd79ayeXMnAN/+9qtcccXs3V5ToWBMpw2i0bQtfPoWT4XrfYunRCKLae5dTvJ8jFYw6KKiwm+7AD/6qI3m5u5+xZqaIMccU23fl1TKIJ02C5bzL5OOjrS9bhhDly89734uFGh9CcH8Nrldfg5efnkryWRXuHoymeXrX/87zzzz70PW3v4YzBMjAvwEGUD/PWAaMghfMQYoFC5YXfFK+WXTlAlAzazZ57JlWlhYaGj2sYV1FIoiLLDILZu57X2cP3+caZpYVu+6MWU9dt2WhWZpbHltCwBTPjFFWrc0zRZehdfb/GEz9S/Ws2n5JpJtSQBCNSGmfW4atafXUjK1/39BRtrAV+wjPClMuCY8puNNFGMb07Robo7T0pLoU2y53V0CJZHIsm5dqy223n+/hVisywFSVORh7txK3n13l/3gdbkcXH31XAIBF8mkjFeSwd8GyaQMAM8HgufL89uTySzt7Sl7fX/hdjv6FWZ9CbdCURePZ7q55R599EP7YR+LZUgkugRST3GVL89kBnar9ofTqREMugkG3VRVBQgG3QQCbgIBly2kuq93lcvlrvL+3Kzbt0e54ornbfHp8Ti5446TbbfxniCtfl1irVCoFQq4QhHXtU9/ZWaB2OvaFo1m7G17e3+Hg8GIr7uAbwNTkELsKOApcglUFaODVGeKto1tNL/VTH17fS/hYtH9n0pe9JCLq+j3/QBQ2DZN08CJbFsBRtpg9a9WgwaTjp/US3R1bO6QIxVf2kjndvnP1VvsRf+sTu3ptVQcVtHv9eSFX7AySMnUkj7dj4q9Jx+Inc2aPPPMBiwL/v3fD9njWBzFwOyJ2GptTbBmTQtr1+5i7doWPv64rZs1Y8KEECedNIE5c8qZPbucSZPCOBwaDzywlgcf/ACASy+dydln1+6XducFWX+CrUu4yeX+RF3h9mg0Q3NzgmQyaweL7wnZrMV9963pt1zTsAVPaamPCRNcgxJNfn/XfvnyfY1zGgzjx4dYuFC3+++SSw7dK+EF0trn9zvwH+DZOkxTxtD1JfrSaYMdO2LcfvtbZLNSpPl8Lu6667QD2sY8g81w/zdd1x8B/lcIsUXXdeVjGQVYlkWsMUbH5g6SHUk7uLzn6LyxwJpH1xDdIUdZrXlsDUdefiTx5rg9UrHloxYAXD4XtQtqmXb6NMYfPX7AfFtGxsDtdxMaH5KTW4/B+7a3dAXNyuDgVCpLKmWSzRo5ISXL5cu0A6WzWdOOucnvk4/DSacNrrrqJTRNw+dzEgp5CiwUMmYoEHBTXOzF43Eqq+NuME2LXbtitLQkiUT6F1umadHQ0OVCXLu2me3bY3Y9TqfGjBmlzJ5dbr/GjfP1ec5LLpnJ3/7WgKbBwoWH7pfrcDg0/H5pfRoKLMvKjeQbSNQZPPbYOjtQO09dXQmXXz6rh5VJiiav1zUq0i4U0lf/yT/pOe9EDsvq2l64j4yjs/r5bnaV5Yvzy13v9FHWfb++j8tv771/4TtotLenuPfedwG44YZjhyXeCwYnvuK6rn8LOA1YrOv615EpJxQjFNMwad/UTue2ToyMgcPpsIXXWKRzeydrH+vKVLLm4TVsf3M7uz7YBZbMRj/xuIlMO30ak06ctNu5E42MgX+cn5IpJQTKx04Afd661JX/R7qBugRS12iyrve8YDLtEUqFw9gtS45YysfS7OnDJj9SDeCxxz7sNcw9P2Kqo0OOrMuLNjkCyoHP57YFmt/voqTEi9/vHnUPvf2BYZg5y5YUW5FI2h4hB11iK5XKsm5dWzcXYmdn2q4nGHQzf34Ns2eXMXt2OYceOm7Qw/E9Hidf+9qRgIXL5ch9PkZ2X2iaZscGDcRhh5X1cst973sn7LV1aG/Jf/fyf1osq7eYyX8XZZoJ+RnIjwzt2p5fduSC++W27373BDRN49BDxxXU17WPTBFSuM1hn3Mg4dNTdA0XP/nJqTz33CbS6RTXX3/ssLVjMN+oLwBfAj4nhGjLJVq9ZGibpdgbMvEMbfVtRHdG7RF9Y9FaYxomiZYEnds76dzRyZpH1mAUBMmaWZNd7++i4rAKahfUMvWUqfhK+v6nXlin5tAIVcvcXH2lkhgJRKNpGhuTbNrUUWBFkqOsDIMCkdSV6yefULHw36rDgf3juyc/hNJyIkeJ7U/q69t7DXM//fTJTJwY7rZfYfByNmsRjaaJSoOnLS5Bw+t15IJsu+J3ioo8hMMe3GPkj8juxJbHI9/b25OsXdvCmjXShfjRR2222wVkUPVxx9Uwe3Y5c+aUM2VK0R6JV/k5hGDQRTjs4ZJLZhIMum0Rb1l5wQBg2cuF26WY6AqF6NrWV3mXAIFCMVJYXricH9nYZb3RNHIB6xqaZg0oGPIM5JbrLoawU2HkKfyDIr93FAif3qKoS+Bo3UZHypGZclRfPsmpy+Xodey+UFdXuk/Hj3R8Phd33nkaGzasH7YcXzAI8SWE2AZ8t2D9+iFtkWKPiTfHaW9oJ9GawOl27tPUNSOFdDRNdGfUFljR7VH5vjNKdGfUnkOxP6qPqubMn5y52/MYGQNv2Et4QlhObj3C/qUnkxl27ozR3p6mvT1JOm2weXOcQKBj9wfnGGjI+lBjGCatrUl27ZL5oeRougS7dnUtNzcniMe7T5iRyZhcdtmzVFcHqaoKUFUVpLIyQFVVwN5WWRnoZq1wODR73bLIuY2yBXUamKZ0u/n9TjyerrQH0p3pwe93j7jPQJ7BiC3Lsti8OcKaNV0uxHzSU5D36JBDSrq5EMvL/YNug4ypMfH5pNAKhdyMG+ejtNQ3omP0eoq2wuXCxK/5PzT55ULhBxY33HAsf//7FtLpNNdddzR+vyf3R6Yr+3s+NUKhNfhgtMSOZM45p47Vq9uHtQ0j8++9YrdYpkXH5g4i2yJkE1kcrtHlWjSzJrGmmBRUO6LdBdaOqD1fYk+8xV7G1Y0jND5EuEaOOHR4HLx6+6u2IHN6nJxwbf+J8/K5uYJVQUqmlOArHtgqdiDJZAx27IjS3p6yR3sVBtu+9VYj27ZFOHT/hNPsNZZl2QHLPYVU4autLTlgMHNRkYfq6iCtrUna27v3eTDoJp02eOedXfQ383hpqS8nzrqLsqqqINXVAUIhj71vocVLjpzqsprl3ZkOh4bX6yzISyXFWXGxNzdVzoETGIZhsmtXnNbWrpit/IMepNhKpw0++KC5mwsx76IFCARcHHNMte1CnDmzbNCxU5ZlkU6buFwaoZCXcNhNUZG3l+gdDRS64/aVe+5ZwIYN65k5s2I/tExxsKLE1ygjm8pK1+KOqJw6wukYtKVry6tbaNvWBvoQN5JchuNISgqr7QUCK/cea4zJtBE9cLgdhKpDlM8sJ1STE1jjw/Zyf1P1dG7r5N0HZRDl7EtmE64J99rHyBq4PC45ufXUfZvcen9hGCaNjTFaW5N0dKSIxTK2GwHo9pBLpw1+/vO3yWQynHfe3CF7AKbTRk5MJXMiKp57T9qiqqUlMWAKAI/HSXm5nzlzpGUl/yor81NR0bWcv4a+hrn/+tefoqYmRDptsGtXnJ074zQ1xWlsjNHYGM+9Yqxf3866da19tiM/ND9vNauqCtpCrbIywLhxvgLXjjzGNC0SiQyJhEylkA/ItizsXEKF7sxQaPCDAAbKjm4YJk1NebGVorMz0ytmq6Mjxfvvd7kQhWjtNry+qirA0UdPtq1a06YVDdoi1dN9GA57KC8PEAqNXGvgcDASrCaK0Y8SX6OEZHuSto1txJvjcg5Ch9Yr3cJAGGmDN37+BplMhmPOO6ZXKoa9wUgbthvQdg/mxdbOKJlY3xMh+Mv8VMyqsAVVqCZkC6xAWWCvpuWZc8kcNvxtA2gwZ+Gc7u3MGPhKfZRPLidUeWCDY3uSH+4vp2jp/YAdSFA9+uiH7NghR6E99ti6bvPmDfbcHR2pXtap7q7AZDfLSU80TVqbJk8uKhBRvpy4ClBRIdfDYc8ePbAHiqfxeJxMmBBmwoTegjp/Xa2tyQJRVijO4uzcGaO+vm83rdvtoKIiYFvPCgVaVVWQigp/r2Ds/LD1SESuF1rNCrO4y0EA7pzVTFrxCueUc7sduxFbGtu2RbslMs0n+ARpxamrK7aF1pw55VRUDG6AyGh1HyoUYwUlvvRnh3EAABpiSURBVEYwlmXRua2TyNYIqc4UTrdzr12LfaViGMz5k23J7sKqwD0Yb45DHy4ll8/VJazGhwhXh203Yag6hGsIghydHifHLj4WNLlsGiaaphGsClI6bfgmt7Ysi7a2JLt2xWlvl5MPFw73zwdF747t26M89piw13vOm5dIZLuJqEJR1SWykt0CrXsSCLgoK/NTW1vczVpV+Bo3zjdkrre9TVPgcGh2+w7rQ4/mXaTdhVl3gfb220191q1pMG6cr0CQSStaYSxaMOjuNgggm83YCUjzVjPQeOSRD+xUBYsXv8iFFx7aTWxpmoUQ7d1SPrS1dQlhn8/FvHlVtgtx1qwyAoP4XI8l96FCMVZQ4msEYmZN2urb6NzRiZk19zlVRM9UDGsfXUvdGXWEa8Jkk9nuFqse7kGjL9eSBsGKIFWHV3UJrJou16Cv1DcsbopJJ0wim8ri9DopnVAqA+iHIdA1EknR2BizxZZpWvYw/8JElnvC3Xe/3WvevK9+9UVKSnw0Nye6ZRvvicOhUVbm45BDSvoVVeXl/kE9yPcneetLPo9TOOzh5puPw+NxMHlykZ3tujBpomma9givwaJpmu1Gmz6975w+qVSWpqZEL5dmflmIVj74oKXPY0Mhdw9B1jVAoLo6QGmpjx07Yjz+eJd4/v3vP2T+/BpaWpK2C3HdutZufVxe7ueUUybZiUzr6ooHZZXqy31YUSGzoiv3oUIxMlDiawSRz0Ifb4qj5YcW7wcXwOt3v94tFYORNnj6y8/gcDnI9BPY7g66KZ5cTKg61C3mKjw+TLAyuF/clvuKZVkYaQOn14mv2Ie3yEuoKoSnIMj6QBCPp9m5M54Lkk9iGKYd3F2Yx2pPSKcNhGjl3Xd3sWZNM6tXN/bap6MjjWlCZWXAdv/lXYHl5QF7vbjYO6yupHzai8LJbvNZvEtLvd1GGM6bV91vPZYlM53H4xmi0bSdwbpLnMl3mYndwu3e/UTNebxeF5MmhZk0qW/XZn7U5s6dXaIsH3+2c2ec7dsHdm06nVov8fyf//mCva5pMG1ase0+nD27nKqqwG7FknIfKhSjEyW+RgDRnVGZhb49idOzf1JFGIbFtq0R6lc0sGNt75FiRjyDo8zP/2/vzoPjvOs7jr+f3X2evSWtDl+Jz8T++ZLtxAkhDs4FIYFpGs6GQguFgZl0CKSUcp9pYVpoOAIZCJNCQ0oJpTRAICWHkziQs0Eh8RH7l1iyLVm2bEvWrdWeT/949lmtbMnWStpdyfq+Zjza1e5qf6u1pY9/x/cbWlWHrzaIpyaIvz5MZGGY6KIooViAoN9HNGwSDvnwzYAf5LZtO42yA14CVQH81X7C88NYofKGLaf8gxO2ensTJBKZ/PKhc6y8+GA6NJTK12LaseM4e/aM3ki9cGGYo0eH8s10TdPDXXddw9Kl1dPzoqaocHnNKd9g5ivSV1VZVFf7p1xfy6l879Tuqq0dvzxCOu1UK+/vT+Yq72dGFZVNJDKk03ZRs2her7M3rKEhRGNj/Sm327ZNX18yF8icfWbu7NmxY0M0N58azKqqLG644bz8EmJkAv9pSCQy+HweIhFLlg+FmMUkfFVIvgr94X5nqcznnfJskm3bHGnv59XtBzj+XDtDezuxx9jjY/g8LPvEZVjjbD6PA/HuBJAglc5iZ+38puOg34s/97E6ahIoYQsNO2s7LX5CZn5mK7wgjBko7/JYKpXh6NEhurvj9PQkGBpKY1kj5R8mum+rUE/PMDt2dObCVif79vXkg5VhwPnn19DY2MCGDfU0NjZQWxsY1TfvPe9ZU5HgNVJh3m354val8xGLBQmHK19d3ufz5JfbxuO2M3KKtKZGLW06DXozJBJONwB3j97pXpdhGFRX+6mu9rNy5alFKp3TnA+STDr/Hi3Lw513vuG01dFl+VCIs5eErzIbqwq91zf50GXbNsc6Bnnl8QMcfeYQQ3uP5wOXtSBCdOMCopsW0P9iB10P7QOg9vUrxg1eJzMLZgXcU17u86ZSNhjgtzwE/D4ClpeA30sw4KU6amEV2QzWDVtWxMJf5cdf7Se6IFr2JU73yH9XVzxfX8k0R375+v3Fj6ejY5CdOztzy4jHR51aM00P69bV0dhYz4YNDaxbN/YsiLshPZVKTVvfvPG4BUktyy1C6iwVRiIWtbWBWd9b0eMZmUWrP3UiKy+VcpoxDwyk8rNm7l60ZNLpZ+n+m/B6jXFn0ZzTnKsLTnOuGRW8ZPlQiLlFwleZTHcV+s5jg7zy6AEOP902OnDNjxDd5AQu/4KR/Svm60P0Pd9OOpOm9vUrpvTc4PZDc1t2QHw4TTxXTTybtUm5R+/9HvyWc+zeb3qJhJ2lTNPnIZvJYmdszIhJoCpAoCZAeH647MVis1k7dzJwiN7eU4tZFhu2bNtpVFw4s3Xs2FD+9mDQx0UXzWfjxgYaGxtYs6Z2QstGluXl5psvoL29fVqWmdxTcE4QcZcKnaBVXe2nqspf1qKiM5FpejFNL1VV/nHvUziLNjiYGncv2tvfvoqHHjpAOp3mbW9bSSZjy/KhEHOUhK8Smu4q9L1dcV7e1sKRp9sYeLkwcIWJblpIdOMC/GMUFwXwmF7mvXUNxzs78ZQ43Hg8Bv5cMEtnbNLxNIPxNNmMTSqVwRvwEaryU9UQIrYwSijsww6Z+KMWlGHJyrZtenoSHDs2SG9vkt7e0eUfij2RmMlkefXVHnbscDbH79zZOapWVnW1n61bz8nPbJ1/fs2kZzO2bFmE1gNnvuNJ40unnUbHzgzWyH6s2toAoZAsY01FMbNo3/721ezdu4+rrloiy4dCzGESvkpgKlXoTzbQO8zuh1s4/GQr/UUGrpNF1s/nRGtyUuOYjIw71pCJGc4tJVb58RSc/usfSNI/kCyoIm5gWZ78iTi37557Km6y+4kGBpK5HokJ+vqSuROJkwtbiUSaPXtO5MPW7t1dxOMjPQTnzw/xmtcszYetJUuiZfklm0xmsG2nqbSzF8uZzaqqsqipCcisSoWZppcbb1xNU9PghDbXCyHOXhK+ptFwzzDdB7oZOj65KvSu+ECCXQ+1cOjJVvp3HRsJXPNygWvTxANXOWXSTmFTM2RihU2saj+BaABjAtnGWcYcCQduU+Tu7pH9MM5pN2cJMxTy5Vu7xGKn7kGKx1McOTJIb6/TIzGVyuS/frGNpgcGkvmTiC+9dBytu0cVK126tCoftDZsqGf+/PCEv3ax3JCaSmVPCag1NX6iUUv2CAkhxAwn4WuKbNum/3A/fW1Tq0KfGEyy+5H9tP3+AL07xw5c1oLIjFqmyKQyeDxezIiJGfIRqAliRSyme4hus2NwgtjQUIqhIaeo6MjpO0++tcvQUIp4fOREomGcvm3PyU6ciLNjR2d+Zqu5uSffHNrjMVi5svAkYj01NaVrzJ1MZvJFQiMRZxbLNKu57LKlM+rvghBCiImT8DVJ2XSW7v3d9B+efBX65FCSPY/u5+D2g/TsPIqdKgxcC4huWjitgatpVxednUMsWTK5x2dSWTw+D2bIxIyYBKsDmOHpD1vFKGyI/MQThzAMuPTSRRPeJG/bNkeODBaEreMcOjSyp8o0PfkZrcZG5yRiqSrBuzN8brX3aNSirs4pklq43Hr8uE+ClxBCzGISvoo01Sr0qXgK/dgB9j9+gO4dHacGro0LsRZO/wxXMpXlnl82k86kueqyLNYZ9jnZNmRTGTyWFytsYYZMgrVBzODM/CuTTGa4444/YRiwefP8cWe6slmbAwd6R81sdXbG87eHwyaXXLKADRuck4hKxUq2VyqZzOD1jhTMrK7209AgJ96EEOJsNzN/k85AU6lCn4qn2Lf9IM2P7efESycFro25Ga4SBK5C9z/ayrETw7nLbbzjuqWjbrdtZzbPa3mcsBW2CMQCmCVogl0KP/3pHo4cGQTg3nv38r73OR2W0+ksr7zSPeokYn//yKGDWCzAFVecS2NjPRs3NrB8+cT65xXLXR4NhXxEo05z44aGENGoJbNYQggxx8yO36wVks1k6TnYQ3978VXoU/EU+59s5dVH9tP14pF84DIbwlRtWkAkt2m+HL94j3bGuf+xQ/nr9z/WxtbN86ivtvAGfFhhCytsEqgN4puFsy6HDw9w770jTYt/8pM99PUl2L+/jz17uhgeHumpt2hRmC1bFuXD1jnnTH/odTfFe71upXWTmpoADQ2hOV83SwghRAnDl1LKA3wP2AgkgA9qrfeNcZ8HgF9rre8s1ViKla9Cf3TA2bA9wSr0qXiKA0+18eojLXS+2IGdq3xtNoRHCp+WKXC5slmbH/zslVF9AlOpLP90504uuHAehmGQzdq5DeU22awTHtzHuh/dDedj3de2x/p4utvGv6/7nMXct7c3Mer1pdNZ7rvP+au2fHk1GzbU55YR62loCE3793i8NjBSTkAIIcRYSjnz9RYgoLW+VCn1WuAbwA0n3ecrQG0Jx1CUdDLNsR3HiHfnqtBPYPkpPZym9Zk29MMtdL5whGw+cIVG6nAtKk/gsm2b4yeGaW4doPlgHy2HBjjQPkC8YObH1XVimG3bWks6HsMgf9rQ3TBuGAZOlQfno/t98XiM/MZ997L72LE+ejxOUVTDcNq6pFKjn1upGF//+uWnrUw+GW5VeMvy5me1YrEAdXVBKfEghBBiQkoZvl4HPAigtX5WKXVR4Y1KqXcAWeB3JRzDhOnfaJIDSUL1oTOeWkwPp2l75hD6kWaON40OXDUbc3W4yhC4unsTNLcN0NzaT8vBPva3D9I/OJJCDAMWL46yeHGUZ589QibjzBSZpofbbruCefNCRYee0R/HDlfu58vFaVr8UL7HnmV5+dKXLp2W4JVKZQCDSMTMz2rNmxciGCxvc28hhBBnj1KGryqgt+B6Rinl01qnlVLrgXcD7wC+ONEvuGvXrmkeYm5giQxP/O0T2FmbdZ9ch2+ME32ZZIaenT10PNdJ/54eSDlBxqix8G2qxbuyCqMhQNwwiGd6oa33lK8xFUPDGQ4dTXCoI8mhownaOhL0DY6e0aqrs7hQ1bBsWYilS4MsWRIiGHSCZE1NhgceOArAtdfOw+/vpre3e1rHWEnXXFOff31vfGMDfX2H6esr7mtkszaZjI1leQiHvbkehz6iUaeyfiIBiQR0dpbgBRSpqamp0kMQUyDv3+wn7+HsV8n3sJThqw8oLMPu0Vq7PVjeC5wDPAYsA5JKqQNa6wdP9wXXr1+P3z+9y0gA27+8naF2p/Fx8oUk6z6QOyk3nKb9uXZe2dZCx/PtZBO5Ga76UL4OVylmuIYTGfYf6qe5dYCWtn5aWvs52jU86j61tQG2bJyPUjFWr65l1arYaYt9Ll9+Hi+88CCpVIqPfOSys66cgfv6DIMJvz63qbQ7q1VV5cxq+f0z+xxKU1MTmzdvrvQwxCTJ+zf7yXs4+5X6PUwkEqedMCrlb5mngOuBn+f2fO10b9Baf9K9rJT6MtBxpuBVKt0t3Tz1tafy13f/126CdUHaX+jg8HOHRgWumq0LnD1c51RNW+BKpbMcbB+gxV0+bOun/ehQfoM7QCRisnnzfFavrmX16lqUilFfHyxqDJbl5eabL6C9vf2sC14w8vrGq2bvlnpwC5hWVVnU1wepqvJLqQchhBBlVcrw9UvgGqXU04ABvF8p9ffAPq31/SV83qI8eMuDpIdHmiJnkhme+/ZzwPQHrkzG5tDRQVpaR2a1Wo8M5vdiAQT8XtatrWPN2rpc0Kpl0aLwtASELVsWofXAme84S23Zsih/2SlgahCJ+PMFTOfNC2FOovWTEEIIMZ1KFr601lngppM+vXeM+325VGOYLLM+xKL3bsJ/7uQDVzZrc7QzTnObE7T2t/Wz/9AAyYKSCD6fwXnLqlBr61i7tg6lalmyJCqn5oqQTmdz+7S8uQbTPiIRt9SDKbNaQgghZpyZvbmlDK67/TpatrWwe9iZfVrtszn3poux6iZeD8q2bbp6Es6yYesAzW397G/rZ6igxIPHY7DknDArV8ZY19jA6jW1rFhRLTMxE+AWLbVtCAR8hEImwaCXUMhpNF1d7ZfvoxBCiFljzoev2IoYF3/8Um776h8xgAtft+CMwau3P5nfn+UuH/YNjC40tXB+iIsuqGbN2jrWbZzHypU1BGZJq55KcRtLezwGwaAvP5MVCjm1tMJhU2YFhRBCzHqSBoAnMenB+aX+pGHyroLbBuNp9rf158KWsym+qycx6vH1MT+vvXAeq1bFWLehgbWN9VLd/DTcze8+n4dQyEcw6MxkRSIWsZifYFCWC4UQQpy95nz4amnp4V+/MVLr44HfH8bweTjeNUxz2wAdx+Oj7l8VMblwfR0rz6th9Zpa1l84n/oStKw5G6RSGWzbwLI8BIMmoZCXYNBZKqypCZyVpy6FEEKIM5nz4euWWx5juOC0Yzpj86tH2gAIBbysW1nDyhXVrFpZw9qNDSxeHsPjlVkZl7sfCwyCQW9uFsuXK1Dqp6rKL82khRBCiAJzPnyNZfniKJ+45QJWrK7DCpnICtjIfiyv15Pf7O6GrFgsSDhs5lsMCSGEEGJ8cz583X771Wzb1pqf/bIsD1/92lYWLoxUeGSV4ZZuME3PqIDl7McK4Pd7ZT+WEEIIMQVzPnytWFHDpz51Mbfe+gwAN96o5kTwcppQG/j9I/WxgkFnqVBKNwghhBClM+fDF8CnP30J99zzMrZtc+ONqtLDKYrTDDpLdqR2K16vB5/PwOczcpdH/tTWWixbVk0s5icSsaR0gxBCCFFmEr5wCnfefvvVDAwky34CL5NxlvmyWTAMMAwDr9fIhScPXq8nd92Tv14YrCzLSyDgIxDw5m8/3d6rZLKVZcuqy/gKhRBCCFFIwlfO9defx4kTcZqaOop63ETCkxOcTr3u9Rr58OT3e/MBS/ZUCSGEEGcvCV8FDMMgnbbxehlz2W70LJQTnvx+Nzz5cvcxJDwJIYQQYlwSvgrEYgGuu265hCchhBBClIzstj6JBC8hhBBClJKELyGEEEKIMpLwJYQQQghRRrNlz5cXIJlMVnocZ4VEIlHpIYgpkvdwdpP3b/aT93D2K+V7WJBXxqxfZdi2XbInny5NTU2vA/5Q6XEIIYQQQhRh6+bNm588+ZOzZebreWArcATIVHgsQgghhBCn4wUW4uSXU8yKmS8hhBBCiLOFbLgXQgghhCgjCV9CCCGEEGUk4UsIIYQQoowkfAkhhBBClNFsOe0opkApZQI/ApYBfuArWuv7KzooMSlKqXlAE3CN1npvpccjiqOU+gzw54AFfE9r/cMKD0kUIfez9Mc4P0szwIfk3+HsoJS6BPia1vpKpdT5wN2ADewCPqy1zpZzPDLzNTf8FdCltd4KvAm4o8LjEZOQ+8H/AyBe6bGI4imlrgS2AJcBVwCLKzogMRlvBnxa6y3APwJfrfB4xAQopT4J/BsQyH3qm8Dnc78TDeCGco9Jwtfc8N/AFwqupys1EDEltwF3AocrPRAxKdcCO4FfAr8BflvZ4YhJeAXwKaU8QBWQqvB4xMQ0A28ruL4ZeCJ3+XfAG8o9IAlfc4DWekBr3a+UigK/AD5f6TGJ4iil/gY4rrV+qNJjEZNWD1wEvBO4CfhPpZRR2SGJIg3gLDnuBe4CvlPR0YgJ0Vr/D6ODsqG1douc9gPV5R6ThK85Qim1GHgc+A+t9U8rPR5RtA8A1yiltgObgHuUUgsqOyRRpC7gIa11UmutgWGgocJjEsX5GM57uArYCPxYKRU4w2PEzFO4vysK9JR7ALLhfg5QSs0HHgZu1lo/WunxiOJprS93L+cC2E1a647KjUhMwpPALUqpb+K0HQnjBDIxe3QzMoNyAjAZp3GymNH+pJS6Umu9HWcf9OPlHoCEr7nhs0AM+IJSyt379SattWzcFqJMtNa/VUpdDvwfzqrDh7XW0qt2dvkW8COl1B9wTqx+Vms9WOExieJ9HLhLKWUBe3C245SV9HYUQgghhCgj2fMlhBBCCFFGEr6EEEIIIcpIwpcQQgghRBlJ+BJCCCGEKCMJX0IIIYQQZSThSwghCiil7s51FJjMY29VSm3NXd6e6+cohBCjSPgSQojpcwVSdFMIcQZS50sIMSPlZo0+BySB5cD9OL313gIYwJtx+iT+NU61+CTwl8AQ0IQThJqBPwKf0Vo/MM7zGMA3gD/DaVruBX6otb5bKfVe4O9w/qPahFMYdVgpdQy4D9iC0xvuPcDlwPeADuCtwHeBNmAdUAPcorX+zfR8d4QQs5nMfAkhZrJLcJpQXwTcjNNc/CJgB/AunCB2pdZ6PfBbnBZabcCngO8DXwKeHi945bwduAAnJL0TOB9AKbUO+BCwRWu9CTgG/EPuMQ3AM1rrDcDPgO9ore/BCXof1FrvzN2vV2t9IfBR4ItT/WYIIc4OEr6EEDPZLq11m9Z6COgE3N6kB3FaZr0beJdS6p+B64EIgNb634F47vaPn+E5rgTu01qntNbHgf/Nff4qYCXwrFLqReAGYHXutmHgntzlHwNXj/O1f5X7uBuoP+OrFULMCdLbUQgxkyVPup4uuLwYeAa4A/gdznLfBQBKqUDudh9wLqBP8xw2zjLmyc/hBX6utf5o7mtGGPmZmdVau3s2PCeNa6zxnvwcQog5TGa+hBCz1cXAPq31t4DncfZZuZvd/wl4DPgYcLdS6nSb4LcBf6GU8iulYsB1uc9vB96qlJqX2xf2fZz9XwAhpdT1ucvvxwl/4IQt+U+tEOK0JHwJIWarhwGPUupl4AVgL7BcKfVanL1bn9Na/wLo4jRLj1rrX+MErV04m/pfzn3+JeBWnBC3GyfY/UvBQ9+plNoBXMtIKHsQuFMptWWaXqMQ4iwkpx2FEKJISilbay3LiEKISZHpcSHEWS9X+PS749z8Zq314XKORwgxt8nMlxBCCCFEGcmeLyGEEEKIMpLwJYQQQghRRhK+hBBCCCHKSMKXEEIIIUQZSfgSQgghhCgjCV9CCCGEEGX0/6JybF5hxH90AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Performing Hyperparameter tuning \n",
    "# Validation Curve\n",
    "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['purple', 'darkblue'])\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,10)\n",
    "ax = plt.subplot(411)\n",
    "viz = ValidationCurve(ExtraTreesClassifier(class_weight='balanced'), ax=ax, param_name=\"max_depth\", param_range=np.arange(1, 11), cv=3, scoring=\"accuracy\")\n",
    "\n",
    "# Fit and show the visualizer\n",
    "viz.fit(X, y)\n",
    "viz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe on the above chart that even though training score keeps rising continuosly, cross validation score drops down at max_depth=7. Therefore, we will chose that parameter for our selected model to optimize its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_model_selection(X, y, ExtraTreesClassifier(class_weight='balanced', max_depth=7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we demonstrated in this article, visualization techniques prove to be a useful tool in the machine learning toolkit, and Yellowbrick provides a wide selection of visualizers to meet the needs at every step and stage of the data science project pipeline. Ranging from feature analysis and selection, to model selection and optimization, Yellowbrick visualizers make it easy to make a decision as to which features to keep in the model, which model performs best, and how to tune model's hyperparameters to achieve its optimal performance for future use. Moreover, visualizing algorithmic output also makes it easy to present insights to the audience and stakeholders, and contribute to the simplified interpretability of the machine learning results."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
